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 Naïve 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
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.
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
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