Archimedes Optimization Algorithm: Heart Disease Prediction

Abstract

Heart diseases are the most important reasons behind the high rate of morbidity and mortality among the world's population. In clinical data analysis, heart disease prediction is represented as an important problem. Progressively, the number of data is increasing, to analyze and processing it is very difficult and specially, it turns out to be to maintain the e-healthcare data. In addition, the prediction model in machine learning is considered as a necessary feature in this paper. Thus, this work concentrates to present a novel heart disease prediction technique by means of considering particular processes such as Feature Extraction, Record, minimization of Attribute, and Classification. At first, in feature extraction, both the higher-order and statistical features are extracted. Then, minimization of attribute and record is performed; to solve the curse of dimensionality the Component analysis Principle Component Analysis (PCA) acts an important role. At last, using the Neural Network (NN) model the prediction process is performed which consumes the dimensionally minimized features. Additionally, one of the main contributions of this article is to work on accurate prediction. Therefore, for the weight optimization of NN, the meta-heuristic techniques are exploited in this work. A novel optimization algorithm named Archimedes Optimization Algorithm (AOA) is proposed which resolves the aforesaid optimization issues. At last, the outcomes of the proposed method states that its efficiency over the other conventional methods.