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

Dr K.Kiran Kumar,P R.Sudha Rani

doi:10.14419/ijet.v7i2.7.10880

Abstract

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.

Highlights

Classification can be defined as a special kind of learning model which is responsible for categorization of different gene-disease datasets
Extreme Learning Machine can be defined as a single-hidden layer feed-forward neural network (SLFN) with learning model [4]
An optimized Particle swarm optimization (PSO) feature selection method is integrated with the weighted ELM model for ensemble learning on microarray datasets

Summary

Introduction

Classification can be defined as a special kind of learning model which is responsible for categorization of different gene-disease datasets. These datasets are classified into set of finite or infinite classes. A learning function generally maps original data into their real-value variable in the process of regression This technique can estimate the predictive variable for every individual sample. Some common factors are generally responsible for errors of machine learning algorithms, those are:- noise, bias and variance. Boosting can be defined as a special kind of machine learning meta-algorithm. This algorithm has the prime objective of reducing bias significantly. EL has two major issues, those are:- 1) This model has over fitting problem and the performance can’t be predicted for unknown datasets. 2) This model is not applicable to binary classification and uncertain datasets

Objectives

Results

Conclusion