Remote Heart Risk Monitoring System based on Efficient Neural Network and Evolutionary Algorithm

Objective: The objective of this paper is to predict the risk level of Heart Disease by applying Probabilistic Neural Network trained with Particle Swarm Optimization in case of Remote Health Monitoring. Methods: In order to achieve the aim of the activity, we propose hybrid model of Particle Swarm Optimization (PSO) and Probabilistic Neural Network (PNN). PSO is a population based meta-heuristic Evolutionary Algorithm (EA) whose goal is to explore the search space in order to find near – optimal solutions for feature selection. The optimal features selected can be used for prediction system to develop a classification model using probabilistic Neural Network. Results: First, we quantify the clinical data set from the UCI machine learning repository and measured the complexity. There are 13 attributes are used such as the age which identifies the age of the person, chest pain type has 4 values, serum cholesterol level, blood sugar, resting ECG results, serum cholesterol level, amount of heart rate achieved, x old peak, number of major vessels colored by fluoroscopy, slope of the peak exercise ST segment, thal, sex, height, weight and additional factor smoking. It has been shown that the time complexity of hybridizing PSO and PNN obtained the promising results compared to other two algorithms such as regression tree and PSO optimization. We also proposed the data mining process to deal with complexity, missing values and high dimensionality followed by incorporating the data mining functionalities like characterization, discrimination, association, classification, prediction and evolution analysis. The experiment carried out in Java on stat log heart disease data set performs better in all noise conditions. Conclusion: The performance was evaluated in terms of time complexity, accuracy, sensitivity and specificity and it proved that the hybrid model of PSO and PNN outperformed the Regression tree and PSO.