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J.Valarmathi1, V.T.Kruthika2 

 

Liver Disease Prediction Model Based on Oversampling Dataset 

with RFE Feature Selection using ANN and AdaBoost algorithms 
 

Ahmed Sami Jaddoa1, Samah J. Saba2, Elaf A.Abd Al-Kareem3 
1 Business Informatics College, University of Information Technology and Communications, Iraq 

2 Department of Computer science, Science of College, University of Diyala, Iraq 
3 Department of Sharia, College of Islamic Sciences, University of Diyala, Iraq 

ahmed.sami@uoitc.edu.iq 1, Samah.j.saba@gmail.com 2, elaaf.ali1989@gmail.com 3 

 

 

Abstract 

Liver disease counts are one of the most prevalent diseases all over the world and they are becoming very 

common these days and can be dangerous. Liver diseases are increasing all over the world due to different 

factors such as excess alcohol consumption, drinking contaminated water, eating contaminated food, and 

exposure to polluted air. The liver is involved in many functions related to the human body and if not 

functioned properly can affect the other parts too. Predication of the disease at an earlier stage can help 

reduce the risk of severity. This paper implemented oversampling dataset, feature selecting attributes, and 

performance analysis for the improvement of the accuracy of classification of liver patients in 3 phases. In 

the first phase, the z-score normalization algorithm has been implemented to the original liver patient 

data-sets that has been collected from the UCI repository and then works on oversampling the balanced 

dataset. In the second phase, feature selection of attributes is more important by using RFE feature 

selection. In the third phase, classification algorithms are applied to the data-set. Finally, evaluation has 

been performed based upon the values of accuracy. Thus, outputs shown from proposed classification 

implementations indicate that ANN algorithm performs better than AdaBoost algorithm with the help of 

feature selection with a 92.77% accuracy. 

Keywords: Machine learning, Classification, Feature selection, RFE, ANN, AdaBoost, and Liver. 

Abstrak 

Hitungan penyakit hati adalah salah satu penyakit yang paling umum di seluruh dunia dan menjadi 

sangat umum akhir-akhir ini dan bisa berbahaya. Penyakit hati meningkat di seluruh dunia karena 

berbagai faktor seperti konsumsi alkohol berlebihan, minum air yang terkontaminasi, makan makanan 

yang terkontaminasi, dan paparan udara yang tercemar. Hati terlibat dalam banyak fungsi yang 

berkaitan dengan tubuh manusia dan jika tidak berfungsi dengan baik dapat mempengaruhi bagian lain 

juga. Predikasi penyakit pada tahap awal dapat membantu mengurangi risiko keparahan. Makalah ini 

mengimplementasikan dataset oversampling, atribut pemilihan fitur, dan analisis kinerja untuk 

peningkatan akurasi klasifikasi pasien hati dalam 3 fase. Pada tahap pertama, algoritme normalisasi z-

score telah diimplementasikan ke kumpulan data pasien hati asli yang telah dikumpulkan dari repositori 

UCI dan kemudian bekerja pada oversampling kumpulan data yang seimbang. Pada tahap kedua, 

pemilihan fitur atribut lebih penting dengan menggunakan pemilihan fitur RFE. Pada fase ketiga, 

P-ISSN : 2715-2448 | E-ISSN : 2715-7199 

Vol.4 No.2 July 2023 

Buana Information Technology and Computer Sciences (BIT and CS) 



Vol. 4, No.2 July 2023 | 86  

 

algoritma klasifikasi diterapkan pada kumpulan data. Akhirnya, evaluasi telah dilakukan berdasarkan 

nilai-nilai akurasi. Dengan demikian, keluaran yang ditunjukkan dari implementasi klasifikasi yang 

diusulkan menunjukkan bahwa algoritma JST memiliki kinerja yang lebih baik daripada algoritma 

AdaBoost dengan bantuan pemilihan fitur dengan akurasi 92,77%. 

 Kata kunci: Pembelajaran mesin, Klasifikasi, Pemilihan fitur, RFE, ANN, AdaBoost, dan Liver 

 

I. INTRODUCTION 

Liver disease can be defined as liver inflammation that results from the actions of bacteria, or toxic 

materials so that liver doesn’t properly operate anymore. According to the reports that have been 

conducted by World Health Organization (WHO) 2005 there has been an estimate that 7.6 million 

patients had died from cancer and 84 million individuals would die over the next decade. This data had 

shown that the liver cancer represents 6th most widespread cancer type worldwide and it is the 3rd-largest 

death cause along with the development. It’s unavoidable that technology development and easier access 

to internet have made it easier to identify liver disease and become big supporters of dealing with special 

need illnesses [1]. Machine Learning (ML) represents an Artificial Intelligence (AI) part that allows the 

system to get knowledge without any explicit knowledge. The supervised algorithms take advantage of 

the human inputs and outputs for prediction accuracy and training process, which is why, they are utilized 

for a variety of the applications of classification. Thus, ML application had extended to the health-care 

also. A very significant problem in the health-care is the rising numbers of the liver disease patients. Liver 

is one of the most vital organs with some functionalities such as detoxification of chemicals, bile 

production, and productions of vital protein types for the blood clotting [2].Feature selection has also 

been referred to as the Instance Selection, Attribute Selection, Variable Selection, Data Selection, Feature 

Construction, or Feature Extraction. It is utilized for the data reduction by redundant and removing 

irrelevant data for increasing data mining accuracy. Feature Selection chooses many relevant features 

from original features [3].Classification has been defined as one of the crucial tasks in DM and ML, due 

to the fact that it is aimed at categorizing every instance in the dataset to distinctive groups on the basis of 

information that has been identified by its features. In addition to that, a major DM task is the data 

classification. it has been attempted to create classifier identifying diabetes at minimal cost and with 

optimal performance [4][5]. 

 

II. LITERATURE REVIEW 

Over the recent years, various researches have been performed to classify liver patients. S. Jain et al. 

[6], proposed a paper based on the Indian Liver Patient Dataset that has a variety of symptoms for around 

600 patients. this work is aimed at the evaluation of several Intelligent Technique outputs, such as K-NN, 

XGBoost, support vector machines (SVM), and decision tree with the ratio of the training set to testing 

set being 80% and 20% respectively. And results have shown that K-NN gives an accuracy of 64%, the 

SVM model gives a 66% accuracy, the Decision Tree model gives an 81% accuracy, and XGBoost gives 

an accuracy of 91%. 

 G. Jamila et al. [7], The proposed model for the prediction of liver cirrhosis sickness employed 

Naive Bayesian, Classification and Regression Tree (CART), and SVMs with 10-fold cross-validation. 

Accuracy, recall, precision, and F1 score were used for the evaluations of the model's performance. 



Vol. 4, No.2 July 2023 | 87  

 

Among all the strategies used in this study, SVM technique produces the optimal results, with an 

accuracy of 73%, precision of 73%, recall of 100%, and F1 score of 84%.  

G. S. Harshpreet Kaur [8], This study has been based upon the prediction of the liver diseases with 

the use of ML algorithms. The prediction of the liver diseases involves many different levels of steps, 

such as: preprocessing, classification and feature extraction. In this paper, a hybrid classification approach 

has been suggested for the prediction of liver diseases, and Data-sets have been collected from Kaggle 

data-base of Indian liver patient records. The suggested model was able to achieve a 77.58% accuracy.  

M. Ghosh et al. [9], aimed at evaluating a number of the ML outputs, such as random forest, logistic 

regression, XGBoost, SVMs, AdaBoost, decision tree and K-NN for prediction and diagnosis of the 

chronic liver disease. The algorithms of the classification have been assessed on the basis of different 

criteria of measurement, like the accuracy, F1 score, precision, recall, area under the curve (AUC), and 

specificity. Amongst algorithms, random forest exhibited superior performance in the prediction of liver 

diseases with 83.7% accuracy.  

N. Nahar et al. [10], analyzed a new and efficient method of ensemble learning for classification of 

liver diseases, where 5 ensemble algorithms, namely AdaBoost, BeggRep, LogitBoost, Begg-J48, and 

random Forest have been implemented and compared based on accuracy, FPR, RMSE TPR, and ROC 

curve. LogitBoost outperformed the rest of the ensemble methods, where its accuracy has been 71.53%.  

This paper has codified an effective process for diagnosing liver disease using deep learning giving it a 

web-based approach. The model attained an accuracy of 67.6 percent and this model predicts whether the 

user is having a liver disease or not. 

 

III. MATERIALS AND METHOD 

 
Fig.1. Overall Process of Liver Disease Model  

 

A. Dataset and Attributes  

Presently, there is a wide range of the data-sets related to liver diseases. In the present paper, ILPD 

has been utilized, it includes 583 rows and 2 classes. Where 1st class is associated with the patient 

records (PRs) of the liver disease and includes 416 records, the 2nd one is for the non-liver (PR) and 

consists of 167 records determined with the use of the summation of every sector field. Fig1 

illustrates the distribution of the data in data-set. In general, the data-set includes 11 columns for 142 

females and 441 male patients. Details have been listed in Table1. 



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Table1. Attributes of the Dataset  

No Attributes  Type Range 

1 Age: Patient Age Interval [4-90] 

2 Gender: Patient Gender  Nominal  [Female- Male] 

3 TB: Total Bilirubin Interval [0.40-75] 

4 DB: Direct Bilirubin Interval [0.10-19.70] 

5 Alkphos: Alkaline Phosphotase Interval [63-2,110] 

6 Sgpt Alamine: Amino-transferase Interval [10-2,000] 

7 Sgot Aspartate: Amino-transferase Interval [10-4,929] 

8 TP: Total Protiens Interval [2.70-9.60] 

9 ALB: Albumin Interval [0.90-5.50] 

10 A/G Ratio: Ratio of Albumin and Globulin  Interval [0.30-2.80] 

11 Selector field * Binary  [1-2] 

 

 

Fig. 1. The number of patients in the dataset 

B. Dataset Pre-Processing  

Pre-processing can be defined as a highly vital stage in ML classification as the cleaner the data, then 

the better are the result of classification tends to be [11]. The methods of preprocessing that have been 

applied in the model can be explained as: 

 a. Reducing noisy data: There are 2 data noise types in ML, which include: class noise and attribute 

noise. None-the-less, for the maximum accuracy in suggested model, the attribute noise is decreased 

for enhanced accuracy with the use of panda library.  

b. Data transformation: which indicates the process of the reorganization or re-structuring of the 

raw data. It’s utilized for the purpose of transforming the raw data to proper format allowing the data 

mining to obtain the strategic information faster and in a more effective way.  

c. Standard scalar: which transforms the data in a way that its distribution has an average value of 0 

as well as a standard deviation that equals to 1. The aggregate functions conduct the operations on 

column values them return one value. 

 

C. Oversampling  

Oversampling refers to the random duplication of the minority class values. As we have already seen, 

the IPLD dataset has 167 non-liver samples and 416 liver samples. Therefore, it may suffer from 

imbalanced class distribution issue that the class of the majority may bias prediction. To overcome 

this problem, Random Oversampling is used to increase the majority of class samples. 

 

D. Feature Scaling  

Feature Scaling normalizes feature values in a pre-defined range. It’s a very vital step for building a 

machine learning model. It reduces the training time and sometimes helps to achieve faster 

1
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convergence for many machines learning. Scaling using mean and standard deviation may suffer if a 

dataset contains too many outliers. We have used the Z-score outlier detection technique to detect the 

outliers and handle those outliers using Robust Scaling.   

 

E. Feature Selection  

Feature selection can be defined as the process of the selection of significant characteristics strongly 

associated with output from data-set for faster model training, decreased dimensionality, reduced 

complexity, improved accuracy and straightforward interpretation. Significant bio-markers/variables 

have been obtained from records of clinical information and lab tests of the patients with the use of 

the ML and statistical data mining algorithms. The abovementioned preprocessing tools include 

packages allowing feature selection [12]. 

 

Recursive Feature Elimination (RFE) 

RFE can be defined as feature selection approach of a wrapper type. Internally, it utilizes filter-based 

approaches; none-the-less, it differs from filter method. It has 2 significant options of configuration, 

which include: i. it determines the number of the features that are to be chosen, ii. it sets ML 

algorithm in the feature selection. In initial case, it searches a sub-set of the features through the 

consideration of all of the features that are present in training data-set and eliminates features until the 

needed number of the features is left. In 2nd case, it utilizes an ML algorithm and ranks characteristics 

based on their significance. It discards least significant features then repeats model fitting steps. The 

entire process is repeated to the point where the stated number of the features is left [13]. 

 

F. Dataset Splitting  

The data-set is split to data for process analysis training and testing. In that, 80% of the data has been 

utilized for the training and 20% of it has been used for the testing. 

 

G. Classification Techniques  

Classification is a model used to predict the future behavior of the data by classifying the records into 

predefined classes. In the classification, precise disease detection with the use of the testing and 

training dataset [14]. It proposed 2 ML models for building prediction. Initially, the training data has 

been trained across 2 ML models, such as Neural Network and AdaBoost are predicted based on a 

trained model of learning, one by one, and after that, test the data. Some of the parameters that 

include the precision, accuracy, and recall are finally compared with some algorithms that have been 

explained above. 

 

a. Artificial Neural Network Classifier  

An ANN [15] is a simulation of the working of the biological neural networks. Each one of the nodes 

has been modeled after a neuron, which is why, it is referred to as artificial neurons as well. An NN is 

made up of several layers, every one of which has a number of the nodes. The typical NN has been 

represented by Fig2. 

 



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Fig2. Diagram of a typical ANN 

 Basically, there are 3 components in the typical ANN: 

• Input Layer – one layer whose number of the nodes is dependent upon the input dimensions. The 

input layer applies a transform to NN’s input and passes that along as input to hidden layers.  

• Output Layer - which is the last layer of an NN, the dimensions of which have been characterized 

by the output. This layer conducts a functionality on hidden layer’s output prior to the production of 

the results. 

• Hidden Layer - Those layers represent the algorithm’s crux. They conduct all of the calculations on 

input for the purpose of producing output. The work of those layers is not known. Which is why, only 

weights and parameters that have been provided to those layers may be tweaked for the purpose of 

producing the needed results.  A network becomes deeper with the increase of the number of the 

hidden layers. Each one of the nodes in a network is referred to as a perceptron, which has been 

depicted in Fig3. A perceptron is made up of 2 parts, which are: a sum of inputs and activation 

function on summation. A certain node takes weighted summation of its inputs then passes it to linear 

or nonlinear activation function. 

 

 

Fig3. A Diagram of the Perceptron 

 

The equation for certain perceptron has been depicted by 1. Weighted summation of inputs (x.w) is 

passed through activation function (f) besides bias value (b). It may be denoted as product of vector 

dot, where n represents the number of the inputs for each node. The activation function produces 

output prediction that has been provided as set of the inputs. Bias term has been added to computation 

for the purpose of helping in the enhancement of the learning of the perceptron.  

z = f (b + x.w) = f (b + ∑ 𝑥𝑖 𝑤𝑖
𝑛
𝑖=1 )                        (1) 

each perceptron utilizes step function as the activation function. In a set of the perceptron’s, which is 

ANN (referred to as the Multi-Layer Perceptron as well), each one of the layers may have a separate 

activation function. 

 



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b. AdaBoost Algorithm 

AdaBoost algorithm includes the use of very short (1-level) decision trees as weak learners added in a 

sequential manner to the set. Every one of the consequent models tries correcting predictions that 

have been made by the model before it in a sequence. It combines several of the average or weak 

predictors for the purpose of building strong predictor [16]. 

 

H. Performance measure 

Performance measure of different machine learning algorithms is analyzed by considering measures 

such as [17]. 

• Confusion Matrix - The confusion Matrix is a table used in performance measures that helps 

in easy visualization as well as in distinguishing true positives, true negatives, false positives 

and false negatives. 

• Accuracy - Accuracy measure is calculated by considering the ratio of the observations that 

have been correctly predicted to total number of the observations. 

                                Accuracy = 
𝑇𝑃+𝑇𝑁

𝑇𝑃+𝑇𝑁+𝐹𝑃+𝐹𝑁
                          (2) 

• Precision - It represents the percentage of true positives out of all the predictions. 

                                Precision = 
𝑇𝑃

𝑇𝑃+𝐹𝑃
                                       (3) 

• Sensitivity - Out of the total positive, what percentage are predicted positive. 

                                Sensitivity = 
𝑇𝑃

𝑇𝑃+𝐹𝑁
                                     (4) 

• Specificity – it represents True negative rate which is the proportion of the negative tuples 

which have been identified correctly. 

                                                  Specificity = 
𝑇𝑁

𝑇𝑁+𝐹𝑃
                                      (5) 

 

IV. RESULTS AND DISCUSSION 

On the implementation of algorithms that have been mentioned in previous section, the following 

results have been obtained: 
Table1. Confusion matrix 

Actual / Predicted Normal Abnormal 

Normal TP FN 

Abnormal FP TN 

 

Table 2. confusion matrix of ANN without RFE Feature selection 

Actual / Predicted Normal Abnormal 

Normal 15 3 

Abnormal 5 60 

 

Table 3. confusion matrix of ANN with RFE Feature selection 

Actual / Predicted Normal Abnormal 

Normal 16 1 

Abnormal 5 61 

 

Table 4. Performance measure of ANN model without and with RFE feature selection 

Model Accuracy Precision Sensitivity Specificity 

ANN without 90.36% 75% 83.3% 92.3% 



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RFE 

ANN with RFE 92.77% 76.1% 94.1% 92.4% 

 

Table 5. confusion matrix of AdaBoost without RFE Feature selection 

Actual / Predicted Normal Abnormal 

Normal 12 5 

Abnormal 6 60 

 

Table 6. confusion matrix of AdaBoost with RFE Feature selection 

Actual / Predicted Normal Abnormal 

Normal 13 4 

Abnormal 5 61 

 

Table 7. Performance measure of AdaBoost model without and with RFE feature selection 

Model Accuracy Precision Sensitivity Specificity 

AdaBoost without 

RFE 

86.74% 66.6% 70.5% 90.9% 

AdaBoost with 

RFE 

89.15% 72.2% 76.4% 92.4% 

 

Fig. 4. Show accuracy of ANN and AdaBoost models without and with RFE feature selection  

 
Fig. 4. Accuracy of ANN and AdaBoost models 

 

V. CONCLUSIONS 

This work presented a model for prediction of liver disease occurrence probability. The analyses and 

evaluations of suggested model have shown that it’s highly sufficient and easy to utilize and implement. 

Two ML algorithms have been applied to ILPD data-set for classified liver patients. In the data 

preprocessing issue of imbalanced class distribution, an oversampling technique (Random Over 

Sampling) is used, and used the Z-score outlier detection technique to detect the outliers and handle those 

outliers using Robust Scaling. Then applied RFE feature selection specifies the number of characteristics 

to be chosen is used for achieving better performance and for achieving an enhanced result, we have 

applied ANN and AdaBoost algorithms. From the analysis of experimental results, the ANN algorithm 

has achieved the highest accuracy of 92.77%. 

 

0,82

0,84

0,86

0,88

0,9

0,92

0,94

ANN without RFE ANN with RFE Adboost without

RFE

Adboost with RFE

Accuracy models 



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