Hybrid approach to medical decision-making: prediction of heart disease with artificial neural network - Bhavekar, Girish Shrikrushnarao; Chafle, Pratiksha Vasantrao; Goswami, Agam Das; Marathula, Ganesh Kumar; Hirve, Sumit Arun; Karpe, Suraj Rajesh; Magar, Nitin Sonaji; Farakte, Amarsinh Baburao; Pikle, Nileshchandra Kalbarao; Shinde, Snehal Bankatrao; Gaikwad, Amit Kamalakar (Institute of Advanced Engineering and Science, 2024)
Heart disease prediction is important in today’s world because it helps to reduce the unpredictable death rate of patients, and cardiac diseases are considered one of the most serious diseases affecting people. Hence, in this paper, a heart disease prediction model is designed for effective prediction of heart diseases by means of machine learning (ML) and deep learning (DL). This prediction uses the proposed method of an artificial neutral network and the Chi2 feature selection method applied to determine which features from the dataset were suitable for prediction. The proposed methodology uses classifiers like support vector machines (SVM), Naive Bayes (NB), logistic regression (LR), random forest (RF), and artificial neural networks (ANN). Python was used to conduct the study that assessed the ANN system proposal with the Cleveland heart disease dataset at the University of California (UCI). Compared to other algorithms, the model achieves an accuracy of 97.64% and takes 0.49 seconds to execute, making it superior in predicting heart disease.