Heart disease classification using optimized Machine learning algorithms

Abstract

Early detection of heart disease is exceptionally critical to saving the lives of human beings. Heart attack is one of the primary causes of high death rates throughout the world, due to the lack of human and logistical resources in addition to the high costs of diagnosing heart diseases which plays a key role in the healthcare sector, this model is suggested. In the field of cardiology, patient data plays an essential role in the healthcare system. This paper presents a proposed model that aims to identify the optimal machine learning algorithm that can predict heart attacks with high accuracy in the early stages. The concepts of machine learning are used for training and testing the model based on the patient's data for effective decision-making. The proposed model consists of three stages, the first stage is patient data collection and processing, and the second stage is data training and testing using machine learning algorithms Random Forest, Support Vector Machines, K-Nearest Neighbor, and Decision Tree) that show The best classification (94.958 percent) with the Random Forest algorithm and the third stage is optimized the classification results using one of the hyperparameters optimization techniques random search that shows The best accuracy was (95.4 percent) obtained also with RF

Synergistic Feature Engineering and Ensemble Learning for Early Chronic Disease Prediction

“…Table I summarizes the selected representative research papers, grouping them by the chronic diseases under investigation and highlighting the ML methods employed. Breast cancer DT, NB, k-NN, and SVM [29] Breast cancer ANN [30] Breast cancer Deep Learning and Light Boosting Classifier [31] Diabetes PyCaret classifiers [32] Diabetes CNN [33] Diabetes RF, NB, and J48 DT [34] Diabetes XGBoost [35] Heart disease RF, SVM, k-NN, and DT [36] Heart disease ANN [37] Heart disease SVM, DT, RF, Gradient Boosting [38] Heart disease RF, k-NN, and AdaBoost [39] Kidney disease DT, k-NN and NB [40] Kidney disease AdaBoost on SVM [41] Kidney disease CNN [42] Kidney disease Stratified Logistic Regression [43] Kidney disease eXplainable AI…”

The Evolving Role of Artificial Intelligence in the Future of Distance Learning: Exploring the Next Frontier

“…Artificial intelligence can automate specific repetitive assignments and process data, saving time and human resources, giving acceptable effects, and creating a great work environment [4][5][6]. Artificial intelligence has shifted from a supplemental tool to a practical one that contributes to the growth of many domains, including health and education [7][8][9]. These techniques assist humans in tasks that require a high degree of accuracy, such as manufacturing and product quality control [10] [11].…”