Early Heart Disease Prediction Using Data Mining Techniques

Authors

  • Dugguh Sylvester Aondonenge Department of Computer Engineering, Faculty of Engineering, Ahmadu Bello University, Zaria, Nigeria
  • Ajayi Ore-Ofe Department of Computer Engineering, Faculty of Engineering, Ahmadu Bello University, Zaria, Nigeria
  • Kamorudeen Hassan Taiwo Department of Family Medicine, Ahmadu Bello University Teaching Hospital, Zaria, Nigeria
  • Abubakar Umar Department of Computer Engineering, Faculty of Engineering, Ahmadu Bello University, Zaria, Nigeria
  • Isa Abdulrazaq Imam Department of Computer Engineering, Faculty of Engineering, Ahmadu Bello University, Zaria, Nigeria
  • Dako Daniel Emmanuel Department of Computer Engineering, Faculty of Engineering, Ahmadu Bello University, Zaria, Nigeria
  • Ibrahim Ibrahim Department of Computer Engineering, Faculty of Engineering, Ahmadu Bello University, Zaria, Nigeria

DOI:

https://doi.org/10.26740/vubeta.v2i2.36735

Keywords:

Data Mining, Heart Disease, Machine Learning Algorithms, Model Performance, Predictive Model

Abstract

This study develops a predictive model for early heart disease detection using data mining techniques to enhance timely and accurate diagnosis. Heart disease prediction is complex due to the need to analyze various risk factors, such as age, cholesterol, and blood pressure. The model integrates multiple machines learning algorithms, including Random Forest, Support Vector Machine, and a hybrid ensemble approach, aiming to achieve higher prediction accuracy and reliability. The methodology follows five phases which include data collection, data pre-processing, feature extraction, model construction, and model evaluation. Data was gathered from publicly available health repositories, preprocessed to remove missing values and irrelevant information, and subjected to feature extraction techniques to identify influential predictors. The data was split into an 80:20 ratio for model training and testing to assess model performance across various classification algorithms. The hybrid model achieved an accuracy of 97.56%, precision of 98.04%, and recall of 97.09%, surpassing the individual algorithms tested. These findings indicate that the hybrid approach effectively supports early intervention for heart disease, particularly in healthcare settings with limited diagnostic resources. The study demonstrates that advanced data mining techniques offer a viable solution for improving patient outcomes through early detection of heart disease.

Author Biographies

Dugguh Sylvester Aondonenge, Department of Computer Engineering, Faculty of Engineering, Ahmadu Bello University, Zaria, Nigeria

Dugguh Sylvester Aondonenge is a data analyst at Federal Inland Revenue Service, in 2017, he obtains a bachelor degree in Computer Science from Federal University Kashere, Gombe State. He further advanced his studies in 2024 where he obtain a Masters degree in Information Technology (MIT) from Ahmadu Bello University, Zaria. His area of interest is Data Analysis and Machine Learning. He can be contacted via email at dugguhsylvester@gmail.com.

Ajayi Ore-Ofe, Department of Computer Engineering, Faculty of Engineering, Ahmadu Bello University, Zaria, Nigeria

Ajayi Ore-Ofe is a lecturer at the Department of Computer Engineering, Ahmadu Bello University, Zaria, Nigeria. He received his MSc and Ph.D from Computer Engineering in Control Engineering, in 2017 and 2022 respectively. He received his MSc and Ph.D from the department of Computer Engineering in Ahmadu Bello University, Zaria, Nigeria. He is mainly research in control engineering. He can be contacted at email: ajayi.oreofe17@gmail.com

Kamorudeen Hassan Taiwo , Department of Family Medicine, Ahmadu Bello University Teaching Hospital, Zaria, Nigeria

Kamorudeen Hassan Taiwo is a Senior Registrar in the department of Family Medicine at Ahmadu Bello University Teaching Hospital, Zaria, Nigeria. He holds a Bachelor of Medicine, Bachelor of Surgery (M.B.B.S) as well as Master’s degree in Disaster Risk Management and Development Studies (MDRMDS) from the Department of Medicine and Department of Geography in Ahmadu Bello University, Zaria, Nigeria, respectively. Dr. Taiwo is an Associate Fellow of National Postgraduate Medical College of Nigeria (NPMCN) and a Fellow of the Institute of Disaster Management and Safety Science, Nigeria (FDMSS). His primary area of research is in clinical medicine with focus on Artificial Intelligence (AI) applications in healthcare. He can be reached at via email at drtaiwokamar@gmail.com.

Abubakar Umar, Department of Computer Engineering, Faculty of Engineering, Ahmadu Bello University, Zaria, Nigeria

Abubakar Umar is a lecturer in the Department of Computer Engineering at Ahmadu Bello University, Zaria, Nigeria. He earned his BEng Degree from Electrical Engineering Department Ahmadu Bello University, Zaria, Nigeria, in 2011, MSc, and Ph.D. degrees from Computer Engineering Department, Ahmadu Bello University, Zaria, Nigeria, in 2017 and 2024. He specializes in various aspects of computer engineering. His primary research focus is in Control Engineering, where he explores the development and optimization of control systems for different applications. He is dedicated to advancing his research and contributing to academic knowledge in this field. He can be contacted via email at abuumar@abu.edu.ng, abubakaru061010@gmail.com

Isa Abdulrazaq Imam , Department of Computer Engineering, Faculty of Engineering, Ahmadu Bello University, Zaria, Nigeria

Department of Computer Engineering, Faculty of Engineering, Ahmadu Bello University, Zaria, Nigeria

Dako Daniel Emmanuel , Department of Computer Engineering, Faculty of Engineering, Ahmadu Bello University, Zaria, Nigeria

Department of Computer Engineering, Faculty of Engineering, Ahmadu Bello University, Zaria, Nigeria

Ibrahim Ibrahim , Department of Computer Engineering, Faculty of Engineering, Ahmadu Bello University, Zaria, Nigeria

Department of Computer Engineering, Faculty of Engineering, Ahmadu Bello University, Zaria, Nigeria

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2025-06-01

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D. Sylvester Aondonenge, “Early Heart Disease Prediction Using Data Mining Techniques”, Vokasi Unesa Bull. Eng. Technol. Appl. Sci., vol. 2, no. 2, pp. 211–226, Jun. 2025.

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