Knowledge Mining from Clinical Datasets Using Rough Sets and Backpropagation Neural Network

The availability of clinical datasets and knowledge mining methodologies encourages the researchers to pursue research in extracting knowledge from clinical datasets. Different data mining techniques have been used for mining rules, and mathematical models have been developed to assist the clinician in decision making. The objective of this research is to build a classifier that will predict the presence or absence of a disease by learning from the minimal set of attributes that has been extracted from the clinical dataset. In this work rough set indiscernibility relation method with backpropagation neural network (RS-BPNN) is used. This work has two stages. The first stage is handling of missing values to obtain a smooth data set and selection of appropriate attributes from the clinical dataset by indiscernibility relation method. The second stage is classification using backpropagation neural network on the selected reducts of the dataset. The classifier has been tested with hepatitis, Wisconsin breast cancer, and Statlog heart disease datasets obtained from the University of California at Irvine (UCI) machine learning repository. The accuracy obtained from the proposed method is 97.3%, 98.6%, and 90.4% for hepatitis, breast cancer, and heart disease, respectively. The proposed system provides an effective classification model for clinical datasets

Hybrid approach using fuzzy sets and extreme learning machine for classifying clinical datasets

Data mining techniques play a major role in developing computer aided diagnosis systems and expert systems that will aid a physician in clinical decision making. In this work, a classifier that combines the relative merits of fuzzy sets and extreme learning machine (FELM) for clinical datasets is proposed. The three major subsystems in the FELM framework are preprocessing subsystem, fuzzification subsystem and classification subsystem. Missing value imputation and outlier elimination are handled by the preprocessing subsystem. The fuzzification subsystem maps each feature to a fuzzy set and the classification subsystem uses extreme learning machine for classification.Cleveland heart disease (CHD), Statlog heart disease (SHD) and Pima Indian diabetes (PID) datasets from the University of California Irvine (UCI) machine learning repository have been used for experimentation. The CHD and SHD datasets have been experimented with two class labels one indicating the absence and the other indicating the presence of heart disease. The CHD dataset has also been experimented with five class labels, one class label indicating the absence of heart disease and the other four class labels indicating the severity of heart disease namely low risk, medium risk, high risk and serious. The PID data set has been experimented with two class labels one indicating the absence and the other indicating the presence of gestational diabetes.The classifier has achieved an accuracy of 93.55% for CHD data set with two class labels; 73.77% for CHD data set with five class labels; 94.44% for SHD data set and 92.54% for PID dataset. Keywords: Extreme learning machine, Fuzzification, Fuzzy set, Classification, Euclidean distance, Membership functio