Analysis of Heart Diseases Dataset using Neural Network Approach

TL;DR

This paper explores using neural networks, including a parallel training approach, to classify heart disease data more efficiently, demonstrating neural networks' effectiveness in medical data analysis.

Contribution

It introduces a neural network-based classification method for heart disease data with a parallel training approach to enhance efficiency.

Findings

Neural networks effectively classify heart disease data.

Parallel training improves classification efficiency.

Neural network approach outperforms traditional methods.

Abstract

One of the important techniques of Data mining is Classification. Many real world problems in various fields such as business, science, industry and medicine can be solved by using classification approach. Neural Networks have emerged as an important tool for classification. The advantages of Neural Networks helps for efficient classification of given data. In this study a Heart diseases dataset is analyzed using Neural Network approach. To increase the efficiency of the classification process parallel approach is also adopted in the training phase.