Using Machine Learning Models for The Prediction of Coronary Arteries Disease - VFAST Transactions on Software Engineering Volume 11 Number 2

Aug 4, 2026

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Coronary heart disease is a leading global cause of mortality for both men and women, and early detection remains a major clinical challenge. To improve timely identification of patients in initial stages, six supervised machine learning classifiers are applied: Random Forest, extreme gradient boost (XGB), Logistic of Regression, Decision Tree, KNN, and Naive Bayes. The study uses a UCI repository dataset and performs preprocessing to remove missing values. It also builds an ensemble combining DT, RF, and XGB, achieving 95.33% accuracy in coronary heart disease prediction.

LanguageEnglish

CategoryHealthcare

FormatPDF

Pages11

Reading Time28 min

AccessFree to download

Abstract

Introduction

Background on coronary heart disease

Motivation and study aim

Methods and experimental setup

Classifiers and dataset

Ensemble design

Results

VFAST Transactions on Software Engineering [http://vfast.org/journals/index.php/VTSE@ 2023](http://vfast.org/journals/index.php/VTSE@ 2023), ISSN(e): 2309-3978, ISSN(p): 2411-6246 Volume 11, Number 2, April-June 2023 pp:149-159 Using Machine Learning Models for The Prediction of Coronary Arteries Disease Muhammad Bilal*, Naeem Aslam, Ahmad Naeem, Muhammad Kamran Abid Department of Computer Science, of Engeerning and Technology, Multan, Punjab, Pakistan *Corresponding author email: [2k19mscs118@nfciet.edu.pk](2k19mscs118@nfciet.edu.pk) ABSTRACT Globally, the leading cause of mortality among both men and women is coronary heart disease. This disease is widely recognized as the primary killer worldwide, and its early detection poses a significant challenge. Given the current state of affairs, it is crucial to promptly identify heart disease in its initial stages to ensure successful patient treatment. Despite numerous attempts by various researchers to develop hybrid and ensemble models for early detection, the desired outcomes have not been achieved. Consequently, the machine learning and algorithmic research community has directed its focus towards improving these methodologies. In this particular study, six supervised machine learning classifiers, namely Random_Forest, extreme gradient boost, Logistic of Regression, Decision_Tree, KNN, and N-Bayes, were employed. The UCI repository dataset was utilized as the sample data, comprising attributes and corresponding values. Data preprocessing techniques were employed to eliminate any missing values. An ensemble model incorporating three algorithms, namely DT (decision-tree), RF (random-forest), and XGB, was constructed. Remarkably, the ensemble model achieved an impressive accuracy rate of 95 .33% for predicting coronary heart disease. KEYWORDS Machine Learning model, Heart disease, Heart failure, Machine Learning classifiers, Ensemble model, Decision Tree, Logistic regression, XGB, KNN, Naive byes JOURNAL INFO HISTORY: Received:May 25, 2023 Accepted: June 27, 2023 Published:June 30, 2023 INTRODUCTION The body of human is prepared by many organs and each organ has a distinct function. The heart is considered asthe central organ of the human being[1] It circulates blood from the heart to extra parts of the body. The human-heart has 4 main functions: pumping oxygenated the blood to the parts of body, pushing hormones and other necessary substances to other organs of the body, it receives deoxygenated blood from the body containing substances. scum and move away. reach the lungs for further oxidation and advancement and also maintain blood pressure. For this purpose, we have three main blood vessels that perform all these functions. The human-heart is main organ of the body of human. If it doesn't work properly, it will affect other organs of body. In one report confirming that 7(million) people expires from attacks of heart per anum. According to the world health organization statement, about 18 million persons died from this disease in 2k15[2] . 31% of deaths are due to heart disease worldwide each year. Pumping of blood in human is an chief purpose of heart, providing O2 and nutrient in human and removing extra metabolic wastes from human body. If anemia in the human body, the heart will not work well causes death of the person. [3] Angina arises if there is a momentary injury in plasma to the human-heart, beginning chest pain. Circulatory disease is of two types. Disease of heart is jam or block your coronary clotting supply paths usually carried out by the development of greasy material called plaque. [4] Coronary vein infection is additionally called coronary illness, ischemic coronary illness, and coronary disease .CHD ensues when plaque builds up in a patient's arteries As plaque continues to build Figure 1 Coronary arteires disease up, the patient's coronary arteries will narrow over time and reduce blood flow to the heart, thereby reducing

FAQs

What machine learning classifiers were used in the study?

The study employed six supervised classifiers: Random Forest, extreme gradient boost (XGB), Logistic Regression, Decision Tree, KNN, and Naive Bayes.

What dataset and preprocessing steps were applied?

It used a dataset from the UCI repository and applied data preprocessing to remove missing values.

How was the ensemble model constructed and what performance was achieved?

An ensemble model combined three algorithms: Decision Tree (DT), Random Forest (RF), and XGB, reaching 95.33% accuracy for predicting coronary heart disease.

Aug 4, 2026

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