An Improved Particle Swarm Optimization based classification model for high dimensional medical disease prediction

Recently, machine learning techniques have become popular and widely accepted for medical disease detection and classification on high dimensional datasets. Classification models is one of the essential model in machine learning models for medical disease prediction due to its fast processing speed, high efficiency and noisy datasets. Traditional machine learning models are failed to estimate the disease patterns with high true positive rate due to large number of features and data size. In this paper, a novel particle swarm optimization based hybrid classifier was implemented for medical disease prediction with high dimensions. The main objective of the feature selection based hybrid classifier is to classify the high dimensional data for large medical feature set. Proposed filtered based hybrid classifier is usually designed and implemented to improve the medical prediction rate on high dimensional data. In this work, we have used different ensemble learning models such ACO+NN, PSO+ELM, PSO+WELM to analyze the performance of proposed model(IPSO+WELM). Experimental results are evaluated on different types of medical datasets including lung cancer, diabetes, ovarian, and DLBCL-Stanford. Performance results show that proposed IPSO+WELM with ensemble model has high computational efficiency in terms of true positive rate, error rate and accuracy.