HEART DISEASE PREDICTION USING HYBRID HARMONY SEARCH ALGORITHM WITH LEVI DISTRIBUTION

Prediction of Heart Disease (HD) gains more importance in the field of medical diagnosis. Generally, experts are required to classify the data to identify the presence of disease or not. The HD is predicted previously with the use of exact algorithms and some heuristic algorithms are also utilized to produce precise results in less computation time. Initially, data mining algorithms are widely used to identify HD. After bio-inspired algorithms have evolved for solving combinatorial optimization problems, the area of HD prediction attracts a number of researchers for solving it. On the other hand, Feature Selection (FS) is a main research area in the field of data classification, which is used to find a smaller set of rules from the training dataset with predefined goals. Several techniques, methodologies include machine learning algorithms, biologically inspired algorithms have been utilized for feature selection. This part of interest motivated us to design an intelligent algorithm based HD prediction by using hybrid models for efficient local search procedure. This paper proposes a hybrid Harmony Search (HM-L) algorithm with Levi distribution to properly predict HD at appropriate time. In this research work, Correlation-based Feature Selection (CFS) is used as a feature selection technique. The effectiveness of hybrid HS algorithm is validated by employing it against a set of datasets. The obtained results of applied datasets without and with feature selection are compared to one another. The simulation results ensure that HSS algorithm achieves better results than the existing methods such as Harmony Search (HM), Biogeography Optimization Algorithm (BBO), Grey Wolf Optimization (GWO), AL Particle Swarm Optimization Algorithm (ALPSO) and Artificial Bee Colony (ABC)