Feature Selection Using Particle Swarm Optimization for Predicting the Risk of Cardiovascular Disease in Type-II Diabetic Patients
Keywords:
Classification, Hybrid Prediction Model, Fuzzy c means clustering(FCM), Principal Component Analysis (PCA), Kullback Leiber Divergence(KLD), Extreme Learning Machine (ELM), Fast Correlation-Based Filter Solution(FCBFS), Particle Swarm Optimization (PSO)Abstract
Diabetes is the most common disease nowadays in all populations and in all age groups. A wide range of computational methods and tools for data analysis are available to predict the T2D patients with CVD risk factors. Efficient predictive modelling is required for medical researchers and practitioners to improve the prediction accuracy of the classification methods .The aim of this research was to identify significant factors influencing type 2 diabetes control with CVD risk factors, by applying particle swarm optimization feature selection system to improve prediction accuracy and knowledge discovery. Proposed system consists of four major steps such as preprocessing and dimensionality reduction of type 2 diabetes with CVD factors, Attribute Value Measurement, Feature Selection, and Hybrid Prediction Model. In proposed methods the pre-processing and dimensionality reduction of the patients records is performed by using Kullback Leiber Divergence(KLD) Principal component analysis (PCA), then attribute values measurement is performed using Fast Correlation-Based Filter Solution(FCBFS), feature selection is performed by using Particle Swarm Optimization (PSO), finally hybrid prediction model which uses Improved Fuzzy C Means (IFCM) clustering algorithm aimed at validating chosen class label of given data and subsequently applying Extreme Learning Machine(ELM) classification algorithm to the result set.
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