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Received January 3 2024, accepted June 9 2025, date of publication September 2 2025.

Original Research Article 
Characterization of Odor Profiles Through the Simplified Binary 
Matching Algorithm for Disease Diagnostics

Joanie B. Houinsou*, Roland C. Houessouvo, Kokou M. Assogba and Daton Medenou

Ecole Doctorale des Sciences de l’Ingénieur (ED-SDI)/Laboratoire LETIA/EPAC, Université d’Abomey Calavi (UAC), Abomey Calavi, 01 BP 
2009 Cotonou, Bénin.

* Corresponding Author Email: jobhouinsou04@gmail.com

ABSTRACT

Background and Objective: This study investigates the characterization of body odor signatures for early disease detec-
tion, aiming to demonstrate the feasibility of using simulated olfactory profiles within a computational diagnostic framework. 
The motivation arises from the growing interest in non-invasive diagnostic alternatives based on volatile organic compounds 
(VOCs) emitted by the human body. Materials and methods: A simulation-based approach was implemented using validated 
VOC datasets to construct binary odor profiles. These profiles were encoded as binary vectors, with each bit indicating the pres-
ence or absence of a specific compound. A simplified binary matching algorithm, excluding mutation and crossover operations, 
was employed to simulate pattern matching. The Hamming distance was used as the fitness function to quantify the similarity 
between profiles. Results and Discussion: The results indicate that the simplified binary matching algorithm reliably identified 
pathological odor profiles, producing high similarity scores with reference signatures. Despite the absence of conventional genetic 
operators, the method consistently converged to optimal or near-optimal matches. These findings emphasize the potential of 
binary odor encoding for distinguishing between healthy and pathological states, underscoring the robustness of the simplified 
computational framework. Conclusion: This work presents a novel and interpretable computational model for olfactory-based 
disease detection using simulated binary VOC patterns. It supports the development of low-cost, non-invasive diagnostic tools in 
medical contexts. Future research should explore extending the method by incorporating continuous VOC encoding, integrating 
evolutionary operators, and validating the results with semi-experimental or clinical data.

Keywords—Body odors, Diseases, Early detection, computational diagnostics, Simplified binary matching algorithm, 
Volatile organic compounds (VOCs).

Copyright © 2025. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY): Creative Commons - Attribu-
tion 4.0 International - CC BY 4.0. The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) 
are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is 
permitted which does not comply with these terms.

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mailto:jobhouinsou04@gmail.com
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Houinsou, Houessouvo, Assogba, Medenou: Characterization of Odor Profiles Through the Simplified Binary Matching 
Algorithm for Disease Diagnostics

INTRODUCTION

The improvement of early disease detection methods 
is of crucial importance for public health.1 Traditional 
approaches to medical diagnosis may be limited by cost, 
accessibility, and reliability. In this context, research is 
increasingly focusing on innovative methods based on 
artificial intelligence to enhance disease detection and 
prevention.2  The genetic algorithm, an artificial intel-
ligence technique inspired by the principles of natural 
evolution, holds promising potential in the field of early 
disease detection. By leveraging the adaptive and evolu-
tionary capabilities of living organisms, this algorithm 
optimizes solutions for complex problems.3

In this context, this article examines the application of 
the genetic algorithm for early disease detection, with a 
specific focus on analyzing body odors composed of vola-
tile organic compounds (VOCs). Recent research suggests 
that certain diseases can alter specific odor profiles of the 
human body,4 providing an opportunity to use olfactory 
information as an early indicator of health issues.

The objective of this article is to present a methodol-
ogy based on a simplified pattern-matching algorithm, 
inspired by the principles of genetic algorithms, for ana-
lyzing body odors and detecting diseases at an early stage. 
Unlike conventional genetic algorithms that incorporate 
selection, crossover, and mutation operations, the method 
implemented here deliberately omits these evolutionary 
components. Instead, it evaluates binary-encoded odor 
profiles using Hamming distance to identify the closest 
match to a target profile. This simplification aims to en-
hance interpretability, reproducibility, and computational 
efficiency within a purely simulation-based framework.
By integrating expertise in genetics, artificial intelligence, 
and medicine, this approach could contribute to revolu-
tionizing medical diagnostic methods by enabling faster, 
more accurate, and less invasive disease detection.

LITERATURE REVIEW

Body Odors

Volatile organic compounds (VOCs) are chemical sub-
stances released by the human body, playing a significant 
role in body odor.5,6 In ancient societies, body odors were 

more prevalent and accepted, regarded as part of individual 
and social identity. Over time, attitudes toward body odor 
have evolved alongside scientific advances and social 
norms.7 In medieval Europe, body odor was associated 
with notions of sin and decadence due to religious beliefs, 
and perfumes were commonly used to mask undesirable 
odors. In the modern era, hygiene and cleanliness became 
priorities, leading to the development of personal care 
products to control body odor. However, these products 
may alter natural odors by adding fragrances.8 Recently, 
certain movements have advocated for the acceptance of 
natural body odor, and challenged social norms that aim 
to eliminate it. Body odors vary among individuals due 
to various factors, and perceptions of body odor differ 
across cultures.9

Early Detection of Diseases

The early detection of diseases plays a crucial role 
in preserving health and well-being. It enables prompt 
intervention by identifying early signs and symptoms, 
leading to more favorable outcomes in terms of treatment, 
management, and even cure.10 Numerous benefits are 
associated with the early detection of diseases.11 Firstly, 
it allows for rapid medical intervention, helping prevent 
disease progression and reduce potential complications.12 
Secondly, it increases the likelihood of treatment success, 
as interventions are often more effective when adminis-
tered at an early stage of the disease. Additionally, it helps 
reduce long-term healthcare costs, as early treatments 
are typically less invasive and less expensive than those 
required at an advanced stage of the disease.13

To detect diseases early, various methods are em-
ployed. Regular screenings and health examinations are 
essential for identifying early signs of common diseases 
such as breast, cervical, and colon cancers. Technological 
advancements have led to the development of sophisti-
cated blood tests and medical imaging techniques, which 
can aid in detecting diseases at an early stage, even in the 
absence of apparent symptoms.14 Furthermore, genetics 
and personalized medicine have opened new possibilities 
by identifying genetic markers associated with certain 
conditions.15

http://framework.By
http://framework.By


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Algorithm for Disease Diagnostics

J Global Clinical Engineering Vol.7 Issue 3: 2025 38

Early disease detection helps limit potential compli-
cations, improve patients’ quality of life, and reduce the 
burden on healthcare systems.12,16

Genetic Algorithm

Genetic Algorithms (GAs) were first described by John 
Holland in the 1960s and later developed by him, his 
students, and colleagues at the University of Michigan 
during the 1960s and 1970s. Holland’s objective was to 
understand the phenomenon of adaptation as it occurs 
in nature and to develop methods for incorporating the 
mechanisms of natural adaptation into computer systems.17

The genetic algorithm (FIGURE 1) is a computational 
approach inspired by the process of biological evolu-
tion. It is a search and optimization method based on 
the principles of natural selection and genetics. Genetic 
algorithms are widely applied to solve complex problems 
in various fields, including engineering, optimization, 
artificial intelligence, and bioinformatics.18,19

The genetic algorithm operates by simulating an arti-
ficial evolution process, in which an initial population of 
individuals (often represented by bit strings) is randomly 
generated. Each individual in the population is evaluated 
based on its performance relative to a specific goal defined 
by an evaluation function.20

The crucial step in the genetic algorithm is selection. 
The fittest individuals, i.e., those with the best perfor-
mance, are chosen to reproduce and produce offspring. 
This selection is typically based on a method called 
“fitness-proportional selection”, in which the probability 
of selection is proportional to the fitness value of each 
individual.17,21,22

Once selection is complete, genetic operations are applied 
to the offspring. These operations include recombination 
(crossover) and mutation. Recombination involves com-
bining the genetic information of two selected individu-
als to create new individuals, while mutation introduces 
random changes in individuals to explore new potential 
solutions.17,21,22 This process of selection, recombination, 
and mutation is repeated over several generations, allow-
ing the population to gradually converge toward increas-
ingly optimal solutions. The genetic algorithm may also 
incorporate techniques such as elitism, which involves 
retaining the best individuals from one generation to the 
next to ensure faster convergence.17,21,22

MATERIALS AND METHODS

Materials

We utilized the Human Metabolome Database (HMDB) to 
obtain detailed information on small-molecule metabolites 
present in the human body. The objective was to apply 
this information for biomarker discovery applications.24,25

FIGURE 1. General workflow of a canonical genetic algorithm.23



39 J Global Clinical Engineering Vol.7 Issue 3 2025

Houinsou, Houessouvo, Assogba, Medenou: Characterization of Odor Profiles Through the Simplified Binary Matching 
Algorithm for Disease Diagnostics

Additionally, we relied on the work of reference26, which 
cataloged over 1,800 volatile organic compounds emitted 
by the body of a healthy individual. We also consulted the 
Cancer Odor Database (COD), an online resource docu-
menting known volatile organic metabolites of cancer 
(VOMC), commonly referred to as “cancer odors”.27

Finally, Broza et al. 28 highlights the increasing im-
portance of developing new diagnostic and detection 
technologies to address growing clinical challenges. It 
emphasizes a new diagnostic frontier based on detecting 
disease-associated volatile organic compounds (VOCs) 
using sensors that employ nanomaterials. 

Population Dataset Construction

To construct the initial population used in our simu-
lations, we compiled a comprehensive dataset of 2,571 
volatile organic compounds (VOCs) from authoritative 
sources, including the Human Metabolome Database 
(HMDB), the Cancer Odor Database (COD), and peer-
reviewed literature, such as the catalog published by.26 
Each VOC was annotated with a unique CAS number and 
labeled according to its known association with physi-
ological or pathological states, including various cancer 
types and healthy conditions.

This dataset served as the basis for generating 25 dis-
tinct binary vectors, each representing a specific simulated 
odor signature associated with a defined condition. The 
encoding process involved mapping the presence (1) or 
absence (0) of each of the 2,571 VOCs for every condition, 
resulting in uniform-length binary chromosomes. These 
chromosomes were stored and processed as the initial 
population from which the algorithm searched for the 
best match to a given target.

The structured nature and dimensional richness of this 
population enabled meaningful comparison and pattern 
recognition through Hamming distance evaluation. Impor-
tantly, the dataset was constructed to balance diversity 
(in terms of represented conditions) and consistency (in 
binary structure), ensuring that the algorithm operated 
within a representative yet tractable search space.

Methods

The method employed for disease detection is the 
simplified binary matching algorithm, which encompasses 
five phases.

Phase 1: Individual Representation

Each individual, denoted by Equation 1, is symbolically 
characterized by a chromosome—a structured sequence 
of fixed-length binary digits that corresponds to the 
quantity of volatile organic compounds (VOCs) defining 
the olfactory profile. The chromosome is mathematically 
expressed as:

     [ ]1 2, , ,i i i inChromosome v v v= …  (1)

Within the confines of this representation (Equation 1), 
each element vij (Equation 2) is discretized into a binary 
bit, serving as an indicator of the presence or absence of 
a specific VOC. This binary encoding is represented by 
the formula:

0,      
1,     ij

If the VOC is not present
v

If the VOC is present


= 


 (2)

where vi1, vi2,..., vin, represent the elements of the chromo-
some for sample i, vij denotes the presence (1) or absence 
(0) of the  j—th VOC, n is the total number of VOCs consid-
ered, and i=1,2, ..., N indexes the sample. 

Consequently, the collective exposition of equations 
(Equation 1) and (Equation 2) coherently explicates the 
chromosome's nature as a combination of binary units, 
delineating the presence or absence of VOCs within the 
context of individual representation. vij is the binary 
variable indicating the state of the j—th VOC in the i—th 
chromosome.

Phase 2: Evaluation Function

An evaluation function assigns a value, or fitness score, 
to each chromosome based on its ability to solve the given 
problem. In this study, the Hamming function serves as the 
objective function (Equation 3), calculating the distance 
between the desired solution and the candidate solution 
within the population.



Houinsou, Houessouvo, Assogba, Medenou: Characterization of Odor Profiles Through the Simplified Binary Matching 
Algorithm for Disease Diagnostics

J Global Clinical Engineering Vol.7 Issue 3: 2025 40

( )
1

0

,

( )

i s

N

ij sj
j

f Chromosome Chromosome

Abs Chromosome Chromosome
−

=

= − −∑
 (3)

where sChromosome denotes the binary vector represent-
ing the i—th individual (candidate solution) in the popula-
tion, Chromosomes the target chromosome corresponding 
to the reference (disease) profile, and f (Chromosomes, 
Chromosomes) the fitness function measuring their simi-
larity. The term Abs(Chromosomeij, Chromosomesj)defines 
the absolute difference between the candidate and the 
target at the position j , while the summation counts

1

0

( )
N

ij sj
j

Abs Chromosome Chromosome
−

=

−∑ the total number 

of mismatches between the two binary vectors.

In our implementation, the fitness score is defined as 
the negative Hamming distance between the target profile 
and each candidate in the population. This transformation 
(multiplication by −1) enables the interpretation of higher 
scores—values closer to zero—as better matches, while 
preserving the relative ranking of similarity. A score of 0 
represents a perfect match, whereas increasingly negative 
scores indicate greater dissimilarity.

Phase 3: Population Initialization

We initialized the population with binary data im-
ported from a specially prepared Excel file, following the 
methodology described in previous studies.24–26,28 These 
data define the search space for disease identification, 
representing the presence or absence of volatile organic 
compounds (VOCs) associated with specific conditions

Phase 4: Main Loop of the Algorithm

The main loop of the algorithm concluded when the 
predefined termination criterion was met, which in our 
case was a fixed number of iterations equal to the popu-
lation size.

Phase 5: Results Analysis

This phase involves examining the individuals to iden-
tify those that correspond to the best solution found and 
extracting relevant information from them to address 
our problem. 

FIGURE 2 illustrates the workflow of a simplified 
pattern-matching algorithm that identifies the binary 
individual within a given population that best matches a 
predefined target profile. 

The procedure begins by initializing variables to store 
the best-known match, then iteratively evaluates the 
Hamming distance between each candidate chromosome 
and the target. Whenever a closer match is identified, the 
best candidate is updated. The algorithm concludes by 
returning the individual with the smallest distance to the 
target. This approach is deterministic, easily interpretable, 
and does not employ stochastic genetic operators such 
as crossover or mutation.

This flowchart (FIGURE 2) illustrates the sequence of 
steps in the proposed deterministic algorithm: 

1. Initialization of the binary-encoded population based 
on VOC presence/absence; 

2. Comparison of each individual with a target profile 
using the Hamming distance; 

FIGURE 2. Workflow of the simplified binary matching algorithm.



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Houinsou, Houessouvo, Assogba, Medenou: Characterization of Odor Profiles Through the Simplified Binary Matching 
Algorithm for Disease Diagnostics

3. Selection of the profile with the minimum distance 
as the optimal match.

The algorithm bypasses traditional genetic opera-
tions—such as selection, mutation, and crossover—and 
relies exclusively on distance-based evaluation.

FIGURE 3 presents the pseudocode for a simplified 
computational procedure designed to identify the individual 
within a binary population that most closely matches a 
given target profile. The method iteratively computes 
the Hamming distance between each chromosome and 
the target, updating the best match whenever a smaller 
distance is encountered. This approach enables efficient 
nearest-neighbor selection in discrete binary spaces while 
eliminating the need for evolutionary operators such as 
crossover or mutation.

RESULTS AND DISCUSSION

Results

The simplified binary matching algorithm, applied 
to early disease detection through body odor analysis, 
produced the following results:

Presentation of Data in Binary Form

We obtained data encoded in binary form, as illustrated 
in Figure 4, representing our search space.

FIGURE 3. Pseudocode for a simplified matching algorithm based on hamming distance.

FIGURE 4. Binary encoding of VOCs related to body odor.



Houinsou, Houessouvo, Assogba, Medenou: Characterization of Odor Profiles Through the Simplified Binary Matching 
Algorithm for Disease Diagnostics

J Global Clinical Engineering Vol.7 Issue 3: 2025 42

FIGURE 5. Algorithm trace for a random healthy chromosome.

FIGURE 6. Fitness scores generated by a random healthy 
chromosome.

FIGURE 7. Algorithm trace for a random diseased chromosome.

In our search space, this chromosome corresponds to an 
individual with neck cancer (FIGURE 10).

Regardless of the target chromosome, the simplified 
binary matching algorithm converges toward an optimal 
solution with an associated score. A score of 0 (FIGURE 
1) indicates that the target chromosome is present in 
the solution space; otherwise, the algorithm identifies 
the chromosome in the space that is closer to the target 
than any other (FIGURE 2). The chromosome in the 

Best Individual and Fitness Score

The code outputs the best individual identified within 
the population of potential solutions—namely, the body 
odor sequence achieving the highest fitness score. This 
score, derived from the Hamming distance between the 
individual and the target sequence, facilitates the identifi-
cation of the most effective profiles for disease detection 
based on body odor. Execution traces of the algorithm are 
presented in Figures 5–12.

Processing by our simplified binary matching algo-
rithm on a randomly generated chromosome classified 
it as healthy, with a fitness score of −1,257 (FIGURE 5), 
indicating a high similarity to the healthy reference profile. 
The corresponding fitness scores for this case are shown 
in FIGURE 6.

Processing by the same algorithm on another randomly 
generated chromosome classified it as diseased, with a 
fitness score of −1,241 (FIGURE 7), indicating slightly 
lower similarity to the healthy reference profile. The 
corresponding fitness scores are presented in Figure 8. 
Within our search space, this chromosome is associated 
with breast cancer.

A diseased chromosome from the search space was 
processed using our simplified binary matching algorithm, 
which identified it with a fitness score of 0 (FIGURE 9). 



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Houinsou, Houessouvo, Assogba, Medenou: Characterization of Odor Profiles Through the Simplified Binary Matching 
Algorithm for Disease Diagnostics

FIGURE 8. Fitness scores generated by a random diseased 
chromosome.

FIGURE 9. Algorithm trace for a selected diseased chromosome 
in the search space.

FIGURE 10. Fitness scores generated by a selected diseased 
chromosome with neck cancer in the search space.

space that is closer to the target than any other (FIGURE 
2).Regardless of the target chromosome, the simplified 
binary matching algorithm converges toward an optimal 
solution with an associated score. A score of 0 (FIGURE 
1) indicates that the target chromosome is present in 
the solution space; otherwise, the algorithm identifies 
the chromosome in the space that is closer to the target 
than any other (FIGURE 2).

Index of the Nearest Data Point
The code identifies the index of the nearest data point 

in the fitness score sequence. This index is then used to 
associate the corresponding data with additional informa-
tion for results analysis. In our case, it links to detailed 
information about the volatile organic compounds asso-
ciated with either a diseased or a healthy individual, as 
determined by their unique CAS  identification number.

Fitness Scores

The code outputs the list of fitness scores for each 
individual in the population at every generation. This 
enables visualization of score evolution over time 
and facilitates tracking of the algorithm’s progress in 
identifying the best individual. As the search space becomes 
more enriched, these scores are expected to improve.



Houinsou, Houessouvo, Assogba, Medenou: Characterization of Odor Profiles Through the Simplified Binary Matching 
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J Global Clinical Engineering Vol.7 Issue 3: 2025 44

Nearest Line
The code retrieves information associated with the 

nearest data point from the Excel file used to generate the 
initial population. This allows for examining the specific 
details of that data and analyzing them in relation to the 
results of the simplified binary matching algorithm.

Algorithm Convergence
Regardless of the target chromosome, the algorithm 

converges towards an optimal solution.

DISCUSSION
By deliberately omitting evolutionary components 

such as selection, crossover, mutation, and replacement, 
the algorithmic procedure in this study deviates from 
conventional genetic algorithms, adopting instead a 
deterministic, pattern-matching framework. The result-
ing model functions solely through the initialization and 
evaluation of a predefined population of binary VOC 
profiles. Each individual in the population represents a 
potential solution encoded as a fixed-length binary vector, 
with evaluation performed using the Hamming distance 
as the fitness metric.

This simplified configuration enhances interpretability 
and reproducibility by avoiding the stochastic variabil-
ity and convergence dynamics inherent in evolutionary 
systems. Although this design sacrifices the exploratory 
capabilities of classical genetic algorithms, it is well-suited 
for simulation scenarios in which the search space is 
predefined and fully enumerable.

To ensure that the dataset retained discriminatory 
power despite the absence of evolutionary mechanisms, 
we conducted a distributional analysis using the Hamming 
distance metric. This analysis assessed profile diversity 
and spatial separability within the binary encoding space. 
The results confirmed that the initial population preserved 
sufficient structural variability to support meaningful 
pattern recognition.

CONCLUSION
In conclusion, the simplified binary matching algorithm 

demonstrates significant potential for early disease de-
tection based on body odors within medical diagnostics. 
Analysis of volatile organic compounds present in body 
odor offers valuable insights into an individual’s health 
status. The study’s results indicate a strong correlation 
between body odor profiles, volatile organic compounds, 
and disease presence, thereby opening new avenues for 
non-invasive and cost-effective diagnostic methods.

However, further studies and the establishment of 
standardized protocols are essential to validate this ap-
proach and ensure its clinical reliability. While the results 

FIGURE 11. Convergence to zero of the algorithm for a target 
chromosome present in the search space.

FIGURE 12. Convergence to a finite score of the algorithm for 
a target chromosome absent in the search space.



45 J Global Clinical Engineering Vol.7 Issue 3 2025

Houinsou, Houessouvo, Assogba, Medenou: Characterization of Odor Profiles Through the Simplified Binary Matching 
Algorithm for Disease Diagnostics

demonstrate the feasibility of body odor analysis for 
early detection of various diseases, additional research 
is necessary to improve the specificity and sensitivity of 
the method.

Integrating the simplified binary matching algorithm 
into body odor analysis offers the potential to optimize 
early disease detection, enabling faster and more effective 
medical intervention. Additionally, this non-invasive ap-
proach may enhance patient acceptance and participation.

Although inspired by the genetic algorithm para-
digm, the implemented model diverges from traditional 
evolutionary computation by adopting a deterministic, 
non-stochastic structure. For clarity, the term “simpli-
fied binary matching algorithm” is used to reflect both 
its origins and methodological constraints.

Overall, the use of the simplified binary matching al-
gorithm for early disease detection based on body odors 
presents promising new prospects in the medical field. This 
approach has the potential to improve treatment success 
rates by enabling early and accurate disease diagnosis.

AUTHOR CONTRIBUTIONS
Conceptualization, J.H. and K.A.; Methodology, J.H.; 

Software, J.H.; Hardware, J.H. and R.H.; Validation, J.H., 
R.H., and D.M.; Formal Analysis, J.H.; Investigation, J.H.; 
Resources, J.H.; Data Curation, J.H.; Writing–Original 
Draft Preparation, J.H.; Writing–Review & Editing, R.H. 
and D.M.; Visualization, J.H.; Supervision, K.A.; Project 
Administration, K.A.; Funding Acquisition, D.M.

FUNDING
This research received no external funding.

DATA AVAILABILITY STATEMENT
Not applicable.

CONFLICTS OF INTEREST
The authors declare they have no competing interest.

ETHICS APPROVAL AND CONSENT TO PARTICIPATE
Not applicable.

CONSENT FOR PUBLICATION
Not applicable.

FURTHER DISCLOSURE
Not applicable. 

REFERENCES
1. Srivastava, S. and Gopal-Srivastava, R. Biomarkers in 

Cancer Screening: A Public Health Perspective. J Nutr. 
2002;132(8 Suppl):2471S–2475S. https://doi.org/10.1093/
jn/132.8.2471S.

2. Maselli, G., Bertamino, E., Capalbo, C., et al. Hierarchical 
convolutional models for automatic pneumonia diagnosis 
based on X-ray images: new strategies in public health. Ann 
Ig Med Prev E COMUNITÀ. 2021;(6):644–655. https://doi.
org/10.7416/ai.2021.2467.

3. Gen, M. and Cheng, R.W. Genetic Algorithms and Engineering 
Optimization. John Wiley & Sons: Hoboken, USA; 1999; 520 p. 

4. Shirasu, M. and Touhara, K. The scent of disease: volatile 
organic compounds of the human body related to disease 
and disorder. J Biochem (Tokyo). 2011;150(3):257–266. 
https://doi.org/10.1093/jb/mvr090. 

5. Smallegange, R.C., Verhulst, N.O., Takken, W. Sweaty skin: an 
invitation to bite? Trends Parasitol. 2011;27(4):143–148. 
https://doi.org/10.1016/j.pt.2010.12.009. 

6. Aksenov, A.A., Gojova, A., Zhao, W., et al. Characterization 
of Volatile Organic Compounds in Human Leukocyte Anti-
gen Heterologous Expression Systems: a Cell’s “Chemical 
Odor Fingerprint”. ChemBioChem. 2012;13(7):1053–1059. 
https://doi.org/10.1002/cbic.201200011.

7. Croijmans, I., Beetsma, D., Aarts, H., et al. The role of fra-
grance and self-esteem in the perception of body odors and 
impressions of others. PLoS One. 2021;16(11):e0258773. 
https://doi.org/10.1371/journal.pone.0258773.

8. Zakrzewska, M. Olfaction and prejudice: The role of body 
odor disgust sensitivity and disease avoidance in under-
standing social attitudes [thesis]. Stockholm University, 
Sweden, 2022. 

9. Mutic, S., Moellers, E.M., Wiesmann, M., et al. Chemosensory 
communication of gender information: Masculinity bias 
in body odor perception and femininity bias introduced 
by chemosignals during social perception. Front Psychol. 
2016;6:1980. https://doi.org/10.3389/fpsyg.2015.01980.

10. Danna, K., Griffin, R.W. Health and Well-Being in the 
Workplace: A Review and Synthesis of the Literature. J 
Manag. 1999;25(3):357–384. https://doi.org/10.1016/
S0149-2063(99)00006-9.

 https://doi.org/10.1093/jn/132.8.2471S
 https://doi.org/10.1093/jn/132.8.2471S
https://doi.org/10.7416/ai.2021.2467
https://doi.org/10.7416/ai.2021.2467
https://doi.org/10.1093/jb/mvr090
https://doi.org/10.1016/j.pt.2010.12.009
https://doi.org/10.1002/cbic.201200011
https://doi.org/10.1371/journal.pone.0258773
https://doi.org/10.3389/fpsyg.2015.01980
https://doi.org/10.1016/S0149-2063(99)00006-9
https://doi.org/10.1016/S0149-2063(99)00006-9


Houinsou, Houessouvo, Assogba, Medenou: Characterization of Odor Profiles Through the Simplified Binary Matching 
Algorithm for Disease Diagnostics

J Global Clinical Engineering Vol.7 Issue 3: 2025 46

11. Pereira, J., Porto-Figueira, P., Cavaco, C., et al. Breath analysis 
as a potential and non-invasive frontier in disease diagno-
sis: an overview. Metabolites. 2015;5(1):3–55. https://doi.
org/10.3390/metabo5010003.

12. Etzioni, R., Urban, N., Ramsey, S., et al. The case for early 
detection. Nat Rev Cancer. 2003;3(4):243–252. https://
doi.org/10.1038/nrc1041.

13. Akil, A.A., Yassin, E., Al-Maraghi, A., et al. Diagnosis and 
treatment of type 1 diabetes at the dawn of the personal-
ized medicine era. J Transl Med. 2021;19(1):137. https://
doi.org/10.1186/s12967-021-02778-6.

14. Haleem, A., Javaid, M., Singh, R.P., et al. Biosensors applications 
in the medical field: A brief review. Sens Int. 2021;2:100100. 
https://doi.org/10.1016/j.sintl.2021.100100.

15. Lee, Y. and Wong, D.T. Saliva: an emerging biofluid for early 
detection of diseases. Am J Dent. 2009;22(4):241–248. 

16. Kang, S., Peng, W., Zhu, Y., et al. Recent progress in under-
standing 2019 novel coronavirus (SARS-CoV-2) associated 
with human respiratory disease: detection, mechanisms, 
and treatment. Int J Antimicrob Agents. 2020;55(5):105950. 
https://doi.org/10.1016/j.ijantimicag.2020.105950.

17. Mitchell, M. An Introduction to Genetic Algorithms. MIT 
Press: London, England; 1996. 221p.

18. Goldberg, D.E. Genetic Algorithms in Search, Optimization, 
and Machine Learning. Addison-Wesley Publishing Com-
pany: Boston, USA; 1989. 412p. 

19. Bayer, S.E. and Wang, L. A genetic algorithm programming 
environment: Splicer. In Proceedings of the 1991 Third 
International Conference on Tools for Artificial Intelligence. 
San Jose, USA, November 1991, IEEE: Piscataway, USA, 
1991. p. 138–139. 

20. McCall, J. Genetic algorithms for modelling and optimisa-
tion. J Comput Appl Math. 2005;184(1):205–222. https://
doi.org/10.1016/j.cam.2004.07.034.

21. Haupt, R,L. and Haupt, S.E. Practical Genetic Algorithms. 
John Wiley & Sons: Hoboken, USA; 2004. 

22. Reeves, C.R. Genetic Algorithms. In Handbook of Metaheuris-
tics. Gendreau M, Potvin JY, editors. Springer: Boston, USA; 
2010; pp. 109–139. (International Series in Operations 
Research & Management Science; vol. 146).

23. Sallah, A., Alaoui, E.A.A., Tekouabou, S.C.K., et al. Machine 
learning for detecting fake accounts and genetic algorithm-
based feature selection. Data Policy. 2024;6:e15. https://
doi.org/10.1017/dap.2023.46.

24. Wishart, D.S., Feunang, Y.D., Marcu, A., et al. HMDB 4.0: 
the human metabolome database for 2018. Nucleic Acids 
Res. 2018;46(D1):D608–617. https://doi.org/10.1093/
nar/gkx1089.

25. Wishart, D.S., Guo, A., Oler, E., et al. HMDB 5.0: the Hu-
man Metabolome Database for 2022. Nucleic Acids Res. 
2022;50(D1):D622–631. https://doi.org/10.1093/nar/
gkab1062.

26. de Lacy Costello, B., Amann, A., Al-Kateb, H., et al. 
A review of the volatiles from the healthy human 
body. J Breath Res. 2014;8(1):014001. https://doi.
org/10.1088/1752-7155/8/1/014001.

27. Janfaza, S., Banan Nojavani, M., Khorsand, B., et al. Cancer 
Odor Database (COD): a critical databank for cancer diag-
nosis research. Database. 2017;2017:bax055. https://doi.
org/10.1093/database/bax055.

28. Broza, Y.Y. and Haick, H. Nanomaterial-Based Sensors 
for Detection of Disease by Volatile Organic Compounds. 
Nanomed. 2013;8(5):785–806. https://doi.org/10.2217/
nnm.13.64.

https://doi.org/10.3390/metabo5010003
https://doi.org/10.3390/metabo5010003
https://doi.org/10.1038/nrc1041
https://doi.org/10.1038/nrc1041
https://doi.org/10.1186/s12967-021-02778-6
https://doi.org/10.1186/s12967-021-02778-6
https://doi.org/10.1016/j.sintl.2021.100100
https://doi.org/10.1016/j.ijantimicag.2020.105950
https://doi.org/10.1016/j.cam.2004.07.034
https://doi.org/10.1016/j.cam.2004.07.034
https://doi.org/10.1017/dap.2023.46
https://doi.org/10.1017/dap.2023.46
 https://doi.org/10.1093/nar/gkx1089
 https://doi.org/10.1093/nar/gkx1089
 https://doi.org/10.1093/nar/gkab1062
 https://doi.org/10.1093/nar/gkab1062
https://doi.org/10.1088/1752-7155/8/1/014001
https://doi.org/10.1088/1752-7155/8/1/014001
https://doi.org/10.1093/database/bax055
https://doi.org/10.1093/database/bax055
 https://doi.org/10.2217/nnm.13.64
 https://doi.org/10.2217/nnm.13.64

