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Machine Learning and Artificial Neural Network for Predicting Heart Failure Risk

Polin Rahman1, Ahmed Rifat1, MD. IftehadAmjad Chy1, Mohammad Monirujjaman Khan1,*, Mehedi Masud2, Sultan Aljahdali2
1 Department of Electrical and Computer Engineering, North South University, Bashundhara, Dhaka, 1229, Bangladesh
2 Department of Computer Science, College of Computers and Information Technology, Taif University, P. O. Box 11099, Taif, 21944, Saudi Arabia
* Corresponding Author: Mohammad Monirujjaman Khan. Email:

Computer Systems Science and Engineering 2023, 44(1), 757-775. https://doi.org/10.32604/csse.2023.021469

Received 04 July 2021; Accepted 22 September 2021; Issue published 01 June 2022

Abstract

Heart failure is now widely spread throughout the world. Heart disease affects approximately 48% of the population. It is too expensive and also difficult to cure the disease. This research paper represents machine learning models to predict heart failure. The fundamental concept is to compare the correctness of various Machine Learning (ML) algorithms and boost algorithms to improve models’ accuracy for prediction. Some supervised algorithms like K-Nearest Neighbor (KNN), Support Vector Machine (SVM), Decision Trees (DT), Random Forest (RF), Logistic Regression (LR) are considered to achieve the best results. Some boosting algorithms like Extreme Gradient Boosting (XGBoost) and CatBoost are also used to improve the prediction using Artificial Neural Networks (ANN). This research also focuses on data visualization to identify patterns, trends, and outliers in a massive data set. Python and Scikit-learns are used for ML. Tensor Flow and Keras, along with Python, are used for ANN model training. The DT and RF algorithms achieved the highest accuracy of 95% among the classifiers. Meanwhile, KNN obtained a second height accuracy of 93.33%. XGBoost had a gratified accuracy of 91.67%, SVM, CATBoost, and ANN had an accuracy of 90%, and LR had 88.33% accuracy.

Keywords

Heart failure prediction; data visualization; machine learning; k-nearest neighbors; support vector machine; decision tree; random forest; logistic regression; xgboost and catboost; artificial neural network

Cite This Article

P. Rahman, A. Rifat, M. IftehadAmjad Chy, M. Monirujjaman Khan, M. Masud et al., "Machine learning and artificial neural network for predicting heart failure risk," Computer Systems Science and Engineering, vol. 44, no.1, pp. 757–775, 2023.



This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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