Heart Disease PredictionA Computational Machine Learning Model Perspective
subject: AdaBoost; Artificial neural network; Decision tree; Ensemble; K-nearest neighbors; Logistic regression; Machine learning; Naive Bayes; Random forest; Supervised; Support vector machine; Unsupervised; XGBoost
description: Relying on medical instruments to predict heart disease is either expensive or inefficient. It is important to detect cardiac diseases early to avoid complications and reduce the death rate. This research aims to compare various machine learning models using supervised learning techniques to find a better model that gives the highest accuracy for heart disease prediction. This research compares standalone and ensemble models for prediction analysis. Six standalone models are logistic regression, Naive Bayes, support vector machine, K-nearest neighbors, artificial neural network, and decision tree. The three ensemble models include random forest, AdaBoost, and XGBoost. Feature engineering is done with principal component analysis (PCA). The experimental process resulted in random forest giving better prediction analysis with 92% accuracy. Random forest can handle both regression and classification tasks. The predictions it generates are accurate and simple to comprehend. It is capable of effectively handling big datasets. Utilizing numerous trees avoids and inhibits overfitting. Instead of searching for the most prominent feature when splitting a node, it seeks out an optimal feature among a randomly selected feature set in order to minimize the variance. Due to all these reasons, it has performed better. 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
creator: Sivakumar S.; Swetha Cordelia A.; Harishwaran S.; Kumar S.; Kokatnoor S.A.
source: Smart Innovation, Systems and Technologies, Vol-351, pp. 281-293.
publisher: Springer Science and Business Media Deutschland GmbH
date: 2023-01-01
rights: Restricted Access
relation: ISSN: 21903018; ISBN: 978-981992467-7
format: Online
language: English
type: Conference paper
coverage: Sivakumar S., Department of Computer Science and Engineering, School of Engineering and Technology, CHRIST (Deemed to be University), Karnataka, Bangalore, 560074, India; Swetha Cordelia A., Department of Computer Science and Engineering, School of Engineering and Technology, CHRIST (Deemed to be University), Karnataka, Bangalore, 560074, India; Harishwaran S., Department of Computer Science and Engineering, School of Engineering and Technology, CHRIST (Deemed to be University), Karnataka, Bangalore, 560074, India; Kumar S., Department of Computer Science and Engineering, School of Engineering and Technology, CHRIST (Deemed to be University), Karnataka, Bangalore, 560074, India; Kokatnoor S.A., Department of Computer Science and Engineering, School of Engineering and Technology, CHRIST (Deemed to be University), Karnataka, Bangalore, 560074, India