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P-ISSN: 2715-2448 | E-ISSN: 2715-7199 
Vol.6 No.1, January 2025 
Buana Information Technology and Computer Sciences (BIT and CS) 
 

 

A Novel Multi-Level Perceptron for Accurate  

Heart Stroke Diagnosis 
 

Muhammad Khubaib1, Muhammad Zaman2*, Tanzila Kahkashan3, Anam Zahoor4, Narges 

Shahbaz5, Shahzad Shoukat6, Fahma Nisar7 
 

1,2,4 Faculty of Computer Science, University of Lahore, 10 KM Lahore- Sargodha Rd, Sargodha, Pakistan. 
3 Faculty of Computing, Universiti Teknologi Malaysia, Johor Bahru 81310, Malaysia. 

5 Department of Education, University of Education, Lahore, Pakistan. 
6,7 Department of Computer Science, COMSATS University Islamabad, Islamabad, Pakistan. 

E-mail: mkhubaib141@gmail.com, muhammad.zaman@cs.uol.edu.pk*, tanzila.kehkashan@cs.uol.edu.pk, 

tanzila@graduate.utm.my, malikanam06@gmail.com, nargesshahbaz20137@gmail.com, karishmaaslam078@gmail.com, 

fahmanisar001@gmail.com 

 
Received: 2024-06-23 | Revised: 2024-12-10 | Accepted: 2025-01-30 

 

Abstract  

Heart is a very important part of human body, it supply blood to body. If the heart fail down the person 

cannot survive. This is very important to diagnose the heart disease timely to start proper treatment. 

To dingoes this disease manually takes time a lot and budget of the patient. Traditionally the patient 

have to go through form different test then he have to give medical history to the doctor then the doctor 

make decision about their disease and then the treatment start. In the developing countries especially 

like Pakistan the income of the people are too much low and they cannot offered different type of 

expensive tests like ECG etc. In this way the disease cannot detect timely and cannot treated properly. 

The heart stroke can be predicted by analyzing different attributes like blood pressure, cholesterol age 

etc., this is a best and easy way to predict heat stroke timely. Different types of Machine Learning and 

deep learning algorithms are used for heart stroke predictions. In this paper we purposed Novel Multi-

Layer-Perceptron (MLP) that are efficient in classification and in heart stoke prediction that model 

achieve high accuracy of 99% 

 

Keywords: Heart disease detection, cardiovascular disease, Classification algorithms, medical 

imaging 

 

 

I. Introduction 

Heart disease is fatal diseases across the world that cause a large number of deaths [1]. This is 

medical condition when heart stop working properly and cannot pump the proper amount of blood to 

the body parts [2]. The primary symptoms of this disease is chest pain, irregular heartbeat, swollen feet 

[3]. The current techniques are not much efficient to detect heart disease early so researcher try to find 

new technologies to overcome these issues and want to detect disease more accurately and timely [4]. 

This is a big issue of diagnosing the disease timely while using current techniques when the medical 

expert not available at that time [5]. If the disease can detect timely this is a big chance to treated this 

and can save the life of patient [6]. 

There are millions of people that are cause of heart disease are diagnosed yearly [3]. There is also 

a big strength of people that are stroked by this fatal disease in United states [1]. The tradition diagnosis 

of heart disease includes collecting the medical history of patient, report of physical health and then all 

these are analyzed by the medial specialist, but this is too much expensive and time taking process [1]. 

The patient has to go laboratories to conducting many tests to finding physical reports, this is also a 



Vol. 6, No.1, January 2025 | 47 

 

 

type of burden in form of money to the patient [7]. The heart disease identification is complex task due 

to tests and other details [8]. 

In developing countries this is also a big issue for patients to get affordable diagnoses due to low 

income and other financial issues, the health care equipment’s and other health facilities are also limited 

in these countries [9]. There are four major methods that are used now a days for heart disease diagnosis 

which include ECG, test of exercise stress, x rays and coronary angiograms [10]. By analyzing  different 

attributes now it is possible to predict the heart store timely these attributes include blood pressure , 

cholesterol  level, age , gender , smoking routine etc. The traditional method for attribute analysis of a 

patient is time consuming and difficult. 

Now a day’s Artificial intelligence play an important role in health different machine learning and 

deep learning algorithms and technologies are now being used to overcome the issues of previous 

manual methods. In heart stroke prediction it play an important role. By using different machine 

learning algorithms now researcher make heart disease prediction models that are cheaper and more 

flexible instead of previous techniques [11], [12], [13]. In this prospectus several machine learning 

approaches using heart disease data sets for training and testing the particular model [14], [15], 

,Decision Tree (Al-Qazzaz, Mohammed et al. 2023), Support Vector Machine are introduced by the 

researchers (Javeed, Rizvi et al. 2020)to predict heart disease . Now by using these models this become 

very easy to predict heart disease timely. Recent technology off deep learning enhances this field and 

increase the accuracy [15].  

II. Methods 

Researchers present eight different type of machine learning models to predict the heart disease. 

The models that they use for classification are Decision Tree, SVM, XG Boost algorithm, Multinomial 

Naive Bayes algorithm, Extra Tree algorithm, Logistic Regression, AdaBoost and Linear Discriminant 

analysis algorithm. There method involves five steps, in step one they get data sets form different online 

data set collections, and secondly, they process the data by refining and standardization, the third step 

was hyper parameter tuning to get height accuracy by achieving hyper parameters best value. In step 

four they apply ML algorithm to classify. In this study the results shows that the accuracy is increased 

by using standardization of data set, in this study the accuracy of different classifier is improved up to 

8.7 percent by standardization the data sets. The overall results Support Vector machine classifier results 

was best form all other classifier and it attain accuracy of 96.72% [16]. 

The authors use different ML algorithm to predict heart disease the algorithms include Support 

vector machine, LR, GBC and KNN. The use Grid Search Cv with these algorithms. They use datasets 

form different source include Beach V UCI Kaggle etc. The results of the study shows that the extreme 

Gradient Boosting Algorithm along with Grid search CV provide a best accuracy for both testing and 

training. The testing and training results was very good 100% and 99%. The study also highlights that 

Using of optimal hyper parameter can enhance the performance of the algorithms [17]. 

This paper present different type of machine learning algorithms with feature selection algorithm 

to predict the heart disease. The algorithms that are used are KNN, Support Vector Machine, LD, GBC 

RF and DT, the algorithm that are used for feature selection is sequential feature selection. 

The results of the study shows that the Random Forest and decision tree provide more accurate 

results then others the results were respectively 100% and 99%. The study also illustrates that by using 

feature selection technique the accuracy of algorithms can be enhance [18]. 

In this research paper the researcher combines two different types of algorithms to propose a model 

for prediction of heart disease, the algorithms that they combine is Random-Forest & SVM. The main 

objective to combine these algorithms was to eliminate the iterative feature for selecting the features 

for the disease. This was done to increase the accuracy of SVM algorithm for diseases prediction. The 



 

Vol. 6, No.1, January 2025 | 48 

 

 

results of this research show that the hybrid model that they purpose gave more accuracy than the 

accuracy of individual algorithm Support Vector Machine and Random Forest [19]. 

In this research the researcher uses different machine learning techniques to predict heart disease, 

they use K-Nearest Neighbor, RF, SVM and Multi-layer Perceptron to attain their objective of 

predicting heart disease. The use different type of techniques such as evaluators of attribute, feature 

elimination and outlier removal to increase the overall performance of these algorithms. They use data 

sets for this purpose from different sources like Cleveland, Long Beach V and UCI Kaggle for their 

model. The result of the heart disease prediction of this model was 82.47 to 100 percent [20]. 

The author presents a new hybrid approach to predict heart disease timely. They combine random 

forest and support vector machine for this purpose. They apply different techniques to risk factors. The 

researcher developer there model by using Jupyter Notebook online.The overall accuracy of this model 

show that the Random Forest classifier gave more accurate result then other algorithms [21]. 

This research article purpose SCA_KNN (Sine Cosine Weighted K-Nearest Neighbour) ML 

algorithm to detect the heart disease in the patients. The model learns from the data that are stored in 

the block-chain. They use block-chain to ensure the integrity of the data of the patients. The results of 

the study shows that this purposed model gave more accuracy than the W K-NN & KNN. The researcher 

also compares this algorithm with other algorithm in many ways like precision, mean square error. F1 

score etc. This purposed algorithm and the storage system that they describe in their research have a 

good effect on increasing the accuracy of heart disease [22]. 

This study, the authors investigated five distinct methods (MMC, Random, Adaptive, QUIRE, and 

AUDI) for deciding which data to include in a multi-label active learning scenario. By selecting the 

most crucial data points to query for their labels, the goal was to lower the expenses of labeling. To 

create predictive models for a dataset of heart disease, these selection techniques were paired with a 

label ranking classifier, and the classifier's hyperparameters were also tuned by using a grid search. 

Overall, the study's findings point to the effectiveness of the selection approach in enhancing the 

learning model's accuracy beyond the data at hand when combined with the label ranking model. 

However, when comparing the models using the F-score, the performance of the selection process was 

very impressive when utilizing the optimum settings [23]. 

Table 1. Literature Review 

Reference Model Name Overall 

Accuracy 

Remarks 

(Absar, Das et al. 

2022) [24] 

Random Forest, 

AdaBoost, KNN, 

Decision Tree 

RF: 99%, DT: 

96%, AB: 100%, 

KNN: 100% 

RF and DT have high accuracy, AB 

and KNN achieve perfect accuracy, 

Utilized Streamlit for computer-

aided prediction system 

(Yilmaz and 

YAĞIN 2022) [25] 

Random Forest, 

Support Vector 

Machine, Logistic 

Regression 

RF: Higher 

Accuracy 

Utilized 10-fold repeated cross 

validation, RF model had higher 

accuracy and sensitivity 

(Qu, Deng et al. 

2022) 

Explainable 

Boosting Machine 

(EBM) 

76% AUC Birth cohort investigation to predict 

congenital heart defects (CHDs), 

Utilized ultrasound screening and 

EBM model 

(Özbilgin, Kurnaz 

et al. 2023) [26] 

Support Vector 

Machine (SVM) 

93% Accuracy Utilized iris analysis and image 

processing for non-invasive CAD 

diagnosis, Demonstrated potential 

for early CAD detection 



Vol. 6, No.1, January 2025 | 49 

 

 

(Bhatt, Patel et al. 

2023) [27] 

Multilayer 

Perceptron, K-Node 

Clustering 

MLP: 87% Utilized GridSearchCV for model 

optimization, achieved high 

accuracy with MLP, Introduced k-

node clustering for improved 

accuracy 

(Nandy, Adhikari et 

al. 2023) [28] 

Swarm-ANN 

Strategy 

95.78% Proposed Swarm-ANN strategy for 

smart healthcare framework, 

Achieved high accuracy and 

outperformed conventional methods 

(Manimurugan, 

Almutairi et al. 

2022) [29] 

Hybrid Linear 

Discriminant 

Analysis, Faster R-

CNN 

HLDA-MALO: 

96.85%, SE-

ResNeXt-101: 

98.06% 

Achieved high accuracy for sensor 

and echocardiogram classification, 

Outperformed other models in 

accuracy 

(MAlnajjar and 

Abu-Naser 2022) 

[30] 

Deep Learning 

Model 

100% Developed model to identify heart 

disease symptoms, Utilized Mel-

Frequency Cepstrum Coefficient 

(MFCC) for feature extraction 

 

A. Datasets 

Our data originates from a 1988 merger of four distinct datasets: the Long Beach V dataset, the 

Cleveland dataset, the Hungarian dataset, and the Swiss dataset. This dataset comprises various 

characteristics, with the "target" property indicating cardiac disease presence—0 for no illness and 1 

for disease. The dataset includes vital information such as age, gender, chest pain type (categorized into 

four values), resting blood pressure, serum cholesterol level (mg/dl), fasting blood sugar status (binary 

for levels above 120 mg/dl), and resting electrocardiographic results (0, 1, 2). Additional attributes 

include the maximum heart rate achieved during observation, signs of exercise-induced angina, the 

slope of the peak exercise ST segment, the number of major vessels colored by fluoroscopy (0 to 3), 

and thalassemia status (normal, fixed defect, reversible defect). 

 
Figure 1. Correlational Heatmap for Heart Stroke Dataset 



 

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B. Preparing Data 

We first eliminate the missing values to prepare quality and reliable data. We removed six items 

in the Cleveland dataset due to missing data. So the record reduces from 331 to 297 records. In the 

following iterations, we focused on shrinking the multiclass values of the predicted attribute, the 

presence of heart disease, into binary values. For this transformation, we took a value of 0 to indicate 

the absence of HD and 1 to indicate the presence of HD. We then standardized the data by converting 

all diagnostic values from 2 to 4 into 1, thus increasing the number of our dataset. In this way, the 

dataset had the quantity of diagnostic values set to 0 or 1, where 0 meant a lack of HD and 1 meant 

existence. The two main parts of the data set are characteristics and the target variable for developing 

our predictive model for heart diseases. 

Standard independent variables that can be featured include age, gender, type of chest pain 

experienced, and other physiology-related parameters. These attributes are given as input to our model 

so that it can learn and give predictions. But the dependent variable, or target variable, will be the output 

for which we are trying to make the prediction: whether a patient has heart disease. Decoupling features 

from the target variables is another essential part of preparing the dataset for training and evaluation 

with models. This would otherwise be an oversight. It enables us to feed the correct data into our model 

so that we can test the predictions with accuracy. After normalization and pre-processing, the data set 

must be divided into training and testing sets. The 80:20 popular approach splits the heart disease data 

set. Eighty percent of the data is used to train the model, and twenty percent to test the model. By using 

the training set, data is given to our model for learning from it and the testing data set is applied to test 

the model for good performance. 

 

C. Multilayer Perceptron (MLP) 

Multilayer perceptron is a primary type of artificial neural network, including layers of interacting 

nodes or neurons. It consists of one input layer, one or various hidden layers, and one output layer. 

There are built-in capabilities to learn complex patterns in the data and relationships among them. In an 

MLP, each neuron receives input, performs a linear combination, applies a non-linear activation 

function, and then passes the results to the next layer. MLPs have been used in nearly all fields of 

classification and regression as well as pattern recognition problems since they can theoretically 

approximate any function that contains discontinuities with appropriate data and computational 

resourcing. In the context of our heart disease prediction, MLPs can learn from these features to classify 

patterns in the data as a sign of the presence or absence of heart disease. 

 
D. Evaluation Measures 

Various Emulation measures were used to determine how much the model could predict heart diseases. 

These are accuracy, precision, recall, and F1-score. 

Accuracy: Percentage of cases that were classified correctly relative to the total number of cases. A 

model is considered dependable in predicting cardiac problems, and the more accurate it is. 

𝐴𝑐𝑐 =
𝑇𝑃 + 𝑇𝑁

𝑇𝑃 + 𝑇𝑁 + 𝐹𝑃 + 𝐹𝑁
                                                      (1) 

Precision: The Accuracy of the model is measured in terms of how many positive instances have been 

correctly predicted by the total amount of positive cases. The exact identification of people with heart 

disease helps avoid many false positives. 

𝑃𝑟𝑒𝑐𝑖𝑠𝑖𝑜𝑛 =
𝑇𝑃

𝑇𝑃 + 𝐹𝑃
                                                                 (2) 

Recall: Sensitivity, also called recall, measures the ratio of true positives that are successfully 

anticipated to actual positives. It shows the model's consistency in the wrong detection of heart 

problems. 



Vol. 6, No.1, January 2025 | 51 

 

 

𝑅𝑒𝑐𝑎𝑙𝑙 =
𝑇𝑃

𝑇𝑃 + 𝐹𝑁
                                                                           (3) 

F1-score: It is a metric of evaluation for a model, although its computation involves recall and accuracy. 

Given that both kinds of wrong outcomes, positive and negative, are taken into account, this would be 

a good test case for models on imbalanced datasets. 

𝐹1 − 𝑆𝑐𝑜𝑟𝑒 =
2 × 𝑃𝑟𝑒𝑐𝑖𝑠𝑖𝑜𝑛 × 𝑅𝑒𝑐𝑎𝑙𝑙

𝑃𝑟𝑒𝑐𝑖𝑠𝑖𝑜𝑛 + 𝑅𝑒𝑐𝑎𝑙𝑙
                                      (4) 

 

III. Results and Discussions 

The way toward an adequate heart disease prediction has been marred with searching for such 

patterns and rigorous evaluation of diverse methodologies. In this work, we tried to take out the mystery 

of the role of machine-learning methods in the detection of subtle patterns that lead to the presence of 

heart diseases while focusing on the factors responsible for predictive accuracy and reliability. 

Table 2. Contribution Results for thew Heart Stroke Prediction with MLP 

 0 1 Macro Avrg Weighted Avg 

Precision   0.97 1.0 0.99 0.99 

Recall  1.0 0.97 0.99 0.99 

F1 score  0.99 0.99 0.99 0.99 

Accuracy  0.99 0.99 0.99 0.99 

 

A. Discussion 

We point out key insights gained through our investigation and what these mean for future research 

and clinical practice. One of the best performers in classification is a Multilayer Perceptron (MLP) 

model, which achieved an accuracy rate of 99%. The suggested model is strong and dependable, as 

shown by the assessment metrics for the models used to diagnose heart disease, which consistently 

show great performance across all measurements. The model was able to get accuracy values between 

0.97 and 1.0 across several test sets, to begin with. Nearly all occurrences that were categorised as 

positive were true, according to precision, which assesses the accuracy of the model's positive 

predictions. Important in medical diagnostics for avoiding needless treatments or further invasive 

procedures, this high accuracy implies that the model is very good at reducing false positives.  

 

 
Figure 2. a) Training Loss and Validation Loss is Plotted Over Graph b) Training Accuracy is Plotted 

against Validation Accuracy 



 

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The model's recall values were 1.0 and 0.97. According to recall, which is a measure of the model's 

accuracy in identifying real positive occurrences, the model does a great job of catching almost all 

genuine cases of heart disease. To guarantee that no occurrence of the illness goes unnoticed, and that 

treatment can begin promptly, high recall is especially critical in medical settings. The F1 score, which 

is a harmonic mean of recall and accuracy, remained constant at 0.99. The model's ability to accurately 

detect instances of heart disease while also minimising false positives is confirmed by this score, which 

strikes a balance between recall and precision. The model's balanced performance and its efficacy in 

sustaining accuracy under varied situations are further shown by the constancy of the F1 score across 

several test sets. 

The model's accuracy was 0.99 across the board, which means that almost all the predictions, 

positive and negative, were spot on. A high level of accuracy verifies that the model is suitable for 

practical use in clinical settings by demonstrating its overall dependability in producing accurate 

predictions. The model's generalizability and robustness are shown by its equal correctness across 

assessments. These features are necessary for a diagnostic tool that is meant to be used in numerous 

real-world circumstances. The findings show that the suggested model for diagnosing heart disease is 

quite effective in terms of accuracy, precision, recall, and F1 score. This impressive performance 

indicates that the model is not only good at detecting instances of heart disease, but also trustworthy in 

preventing false positives, guaranteeing thorough and precise diagnoses. Since this is the case, the 

model may be relied upon by medical practitioners to reliably diagnose cardiac illness. That very high 

sensitivity level shows the strength of MLP in correctly defining cases of heart disease. Furthermore, 

MLP has represented good accuracy, precision, recall, and F1-score metrics with the precision of 

classifying an individual with or without heart disease, which would be remarkable. 

 
Figure 3. Confusion Matrix 

For all the measures, these models showed strong performance, which strengthens the effectiveness 

of collaborative intelligence in raising predictive accuracy and reliability. 

B. Comparative analysis  

Recent developments in machine learning and deep learning are shown by the significant 

discrepancies in accuracy found when comparing different models for the detection of heart disease. In 



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their study [38], found that traditional models like Logistic Regression (LR), K-Nearest Neighbours 

(KNN), Support Vector Machine (SVM), Random Forest (RF), Decision Tree (DT), and a general Deep 

Learning (DL) approach had accuracies ranging from 83.3% to 94.2%. The Deep Learning model is the 

most impressive of the bunch, with an accuracy rate of 94.2%. On the other hand, ensemble approaches 

show that they are more effective. As to the findings of Atallah and Al-Mousa [31], the Hard Voting 

Ensemble model—which integrates many classifiers—attained a precision of 90.00%. Better forecasts 

are produced by this strategy since it takes advantage of combining the capabilities of many models. 

While the ensemble methods outperform the individual classical models, the Naive Bayes (NB) 

classifier (84.51 percent accuracy) and the KNN classifier (85 percent accuracy) also perform 

comparably, according to [32] and [33], respectively. The accuracy of 88.70% achieved by [34] when 

Decision Tree and Random Forest models were combined shows the effectiveness of ensemble 

approaches in improving prediction accuracy. Additionally, ANNs have been investigated; however, 

[35] reported an accuracy of 82.49% using ANNs, suggesting that neural network topologies for the 

detection of cardiac disease need additional optimisation [36]. demonstrated an accuracy of 88.70% 

using a linear model and Random Forest, demonstrating the efficacy of hybrid techniques.  

According to what [37] stated, one remarkable model, LOFS-ANN (Local Outlier Factor-Support 

Artificial Neural Network), managed to reach an accuracy level of 90.5%. This methodology improves 

neural networks' forecasting abilities by using anomaly detection. The suggested model in this research 

achieves a remarkable 99% accuracy, far surpassing all the preceding models. Significant advancements 

in model design and training approaches, maybe using state-of-the-art techniques like ConvMixer for 

effective feature extraction and classification, are indicated by this. All things considered, the 

comparison study shows how heart disease diagnostic models have progressed, and the suggested model 

is the most accurate one yet for this vital medical application.  

 

Table 3. Comparison of Contributing results with previous studies 

Model Accuracy Reference 

LR, KNN, SVM, RF, DT, DL 83.3%, 84.8%, 83.2%, 

80.3%, 82.3%, 94.2% 

(Bharti, Khamparia et al. 2021) 

Hard voting ensemble 90.00% (Atallah and Al-Mousa 2019) [31] 

NB 84.51% (Tougui, Jilbab et al. 2020) [32] 

KNN 85.00% (Pawlovsky 2018) [33] 

RF+DT 88.70% (Kavitha, Gnaneswar et al. 2021) [34] 

ANN 82.49% (Almazroi, Aldhahri et al. 2023) [35] 

RF with a linear model 88.70% (Mohan, Thirumalai et al. 2019) [36] 

LOFS-ANN 90.5% (Goyal 2022) [37] 

Proposed Models 99% Purposed model 

 

IV. Conclusion  

In summary, our research represents one giant stride toward realizing the transformational potential 

of machine learning in predicting heart diseases. We have demonstrated the efficacy of machine 

learning models for augmenting traditional approaches in heart disease diagnosis and risk assessment 

through careful experimentation, rigorous evaluation, and nuanced interpretation. We unravelled some 

novel insights into the complex interplay of factors contributing to heart disease manifestation and 

progression by applying state-of-the-art methodologies and a multidisciplinary approach. Our results 

are, therefore, more than a technical tour de force; they also demonstrate the transformational impact 

machine learning in healthcare can enable for proactive and personalized healthcare interventions. As 

such, the promise of further increasing predictive accuracy, reliability, and interpretability with 



 

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continued exploration and innovation in machine learning techniques lies in these critical dimensions: 

ultimately advancing improved patient outcomes and better clinical decision-making. 

 

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