Improving Heart Disease Prediction Using Random Forest and AdaBoost Algorithms

Halima El Hamdaoui,Mustapha Maaroufi,Nour El Houda Chaoui,Badr Alami,Said Boujraf

doi:10.3991/ijoe.v17i11.24781

Abstract

heart disease is a major cause of death worldwide. Thus, diagnosis and prediction of heart disease remain mandatory. Clinical decision support systems based on machine learning techniques have become the primary tool to assist clinicians and contribute to automated diagnosis. This paper aims to predict heart disease using Random Forest algorithm enhanced with the boosting algorithm Adaboost. The model is trained and tested on University of California Irvine (UCI) Cleveland and Statlog heart disease datasets using the most relevant features 14 attributes. The result shows that Random Forest algorithm combined with AdaBoost algorithm achieved higher accuracy than applying only Radom Forest algorithm, 96.16%, 95.98%, respectively. We compare our suggested model to report machine learning classifiers. Indeed, the obtained result is supporting the efficiency and validity of our model. Besides, the proposed model achieved high accuracy compared to existing studies in the literature that confirmed that a clinical decision support system could be used to predict heart disease based on machine learning algorithms.

Highlights

Technological innovations contribute to empowering, enriching, and significantly transforming the health work methods
Prediction models using machine learning (ML) have been suggested by various studies to diagnose different diseases such as lung cancer, Liver disease, breast cancer, obesity, Parkinson, Alzheimer, and cardiovascular diseases (CVDs)[5]–[12]
We propose a combination of Adaptive Boosting (AdaBoost) with Random Forest (RF) as a base decision tree

Summary

Introduction

Technological innovations contribute to empowering, enriching, and significantly transforming the health work methods. A predictive analytic model is used to assist clinicians to make more accurate predictions based on the volume of information gathered through a clinical data; such as data from past treatment and medical research results [4]. These models can be prospectively installed within the clinical settings to investigate whenever patients risk developing diseases. CVDs in Morocco represented 38% of total causes of deaths in 2016 (Figure 1)[13] This high mortality rate has attracted significant attention during the last years to improve and automate CVD diagnosis, resulting in numerous approaches

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Conclusion