Z-Score Normalized Feature Selection and Iterative African Buffalo Optimization for Effective Heart Disease Prediction

Abstract

At present, health prediction in contemporary life set off very much indispensable. Big Data exploration plays a major contribution to predict subsequent status of health and offers outstanding health consequence to people. A lot of research is persisting on predictive analytics utilizing optimized machine learning techniques to disclose healthier decision making. Big Data analytics strengthens exceptional opening to predict future health condition from health criterions and bestow finest outcomes. We used Big Data Predictive Analytic model for heart disease prediction using Z-score Normalized Iterative African Buffalo Optimization (ZN-IABO). It fills the missing values in the database based on Z-score Normalized Data Pre-processing that standardize the scores on same scale by dividing score deviation by standard deviation. Z-score normalization model is suitable for huge data sets especially for big data. Next, Iterative African Buffalo Optimization based Feature Selection process is applied to the preprocessed data that addresses pre-mature convergence. In the simulation, the proposed ZN-IABO method is also compared with numerous well-known algorithms ZN-IABO (without optimization), imperialist competitive algorithm, and FODW. The result illustrates that this proposed algorithm is very competitive compared with the cardiovascular disease dataset for addressing the theoretical issue and superior to solving the real-world issue. The proposed ZN-IABO method (with optimization), achieves enhancement in the heart disease prediction accuracy by 17%, minimization of heart disease prediction time, error rate, and space complexity by 22%, 39%, and 37% as compared to the ZN-IABO (without optimization), imperialist competitive algorithm, FODW, MLBO respectively