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  MFES-NB Framework for Detection of Heart Disease using Data Mining Technique  
  Authors : Abba Babakura; Mahmud Ahmad Yusuf; Baba Yachilla Alhaji
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Huge amount of data containing information deemed to be useful for effective decision making are collected and stored by health care industries. The heart disease (HD) has been considered one of the complex and deadliest human diseases in the world. An accurate and timely diagnosis of heart disease is crucial for heart disease prevention and treatment. Machine learning algorithms have proven to be crucial in providing the desired solutions. However, it suffers from finding the best combination of features that improves the classification accuracy of heart disease and maintain a balanced feature selection. In this paper, we propose new machine learning based diagnosing framework that provides an improved feature selection approach and classification algorithms for the prediction of heart disease. The framework combines Multiple Feature Evaluation System (MFES) and Nave Bayes (NB) algorithm for the experimentation on the heart disease dataset. The result showed that the MFES selects the best features and also, improves the performance of the classification algorithm. In addition, the NB algorithm results showed an improvement in the prediction of accuracy of the heart disease. The framework will assist the doctors in the diagnosing of patients efficiently.

 

Published In : IJCSN Journal Volume 9, Issue 3

Date of Publication : June 2020

Pages : 101-108

Figures :06

Tables : 01

 

Abba Babakura : obtained the B.Eng. degree in computer engineering from University of Maiduguri, Nigeria. He also received the MSc degree in intelligent systems from Universiti Putra Malaysia, Malaysia. He is currently doing his PhD degree in computer science from Usmanu Danfodiyo University Sokoto, Nigeria. His primary research interests include artificial intelligence, machine learning and intelligent building.

Mahmud Yusuf : obtained the BSc degree in computer science from Bayero University Kano, Nigeria. He also received the MSc degree in computer science from Universiti Putra Malaysia, Malaysia. He is currently doing his PhD degree in computer science from School of Computing and Engineering, University of Huddersfield, United Kingdom. His primary research interests include artificial intelligence, machine learning and data mining.

Baba Yachilla : obtained the B.Eng. degree in electrical and electronics engineering from University of Maiduguri, Nigeria. She also received the MSc degree in Electrical and Electronics Engineering from Nile University of Nigeria, Nigeria. She is currently doing her PhD degree in electrical and electronics engineering from Nile University of Nigeria, Nigeria. Her primary research interests include artificial intelligence, control system and machine learning.

 

Heart disease, Nave Bayes algorithm, Feature selection, Classification

We have presented a new framework, MFES-NB, to address the issue of heart disease diagnosis. The model introduces a number of experiments to evaluate its performance. This system can help medical practitioner in efficient decision making based on the given parameter. We have train and test the system using a stratified 10- folds cross validation and obtained an accuracy score of 72%. This model demonstrates promising result and gives the patient to have early detection of heart disease presence.

 

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