An Effective Classification Algorithm for Heart Disease Prediction with Genetic Algorithm for Feature Selection
Samina Kanwal, Junaid Rashid, Muhammad Wasif Nisar, Jungeun Kim, Amir Hussain
Abstract
Heart disease is the world’s leading cause of increasing death rates. Although there is a lot of research in the medical sector, an efficient and reliable model to predict this disease at an early stage is still required. So, early diagnosis of heart disease is the most promising strategy for effective treatment. In this paper, we utilize the Genetic algorithm (GA) to select attributes, which are used as input for the machine learning algorithms Deep Learning(DL), Support Vector Machine (SVM), Neural network (NN), Naive Bayes (NB), and Logistic regression (LR). The two datasets of heart disease are used for model implementation. The results evaluation is measured using accuracy, precision, and f-measure. The proposed model achieves the 92% result in the term of accuracy. In terms of precision, 96% result is achieved.