Artificial Neuro Probit Regression Based Associative Frequent Pattern Mining for Heart Disease Prediction

Abstract

Data mining is a technique used to explore the big data from the different sources to obtain the hidden patterns through statistical technique and machine learning methods. Due to implication in the development of a variety of applications, data mining algorithms are applied to a healthcare domain for the prediction of disease at an earlier stage. The various data mining applications have been useful for medical experts to diagnose disease in an accurate method is a challenging one. To improve the accuracy in heart disease prediction using Artificial Neuro Probit Regressed Associative Frequent Pattern Mining (ANPRFPM) model is introduced. Initially, the number of attributes and the data are collected from dataset in the ANPRFPM model (i.e. patterns). The ANPRFPM model trained by the input layer, hidden layer, and the output layer using large set of attributes and patterns. Input layer contains set of attributes and patterns are given as input to the network. In hidden layer of the proposed ANPRFPM model contains probit regressed associative rule mining concept to identify the frequent or non-frequent itemsets based on the support value. Continued by, the confidence is measured between the training and testing patterns of disease and conveyed into the output layer. Finally, soft sign activation function at the output layer for analyzing the confidence value and providing the two classification outcomes as normal or abnormal with higher accuracy in ANPRFPM model. To revolve, the error in the disease prediction gets minimized at the output layer. Using a heart disease dataset with different performance metrics an experimentation is carried out such as prediction accuracy, false-positive rate and prediction time with respect to a number of patterns. In an ANPRFPM model achieves higher prediction accuracy with minimum time as well as the false positive rate than the state-of-the-art methods proves an observed result.