Analysis of Heart Diseases Dataset using Neural Network Approach

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.

💡 Research Summary

The paper “Analysis of Heart Diseases Dataset using Neural Network Approach” presents a case study in which the authors apply a feed‑forward artificial neural network (ANN) to the well‑known Cleveland heart disease dataset (13 clinical attributes, 414 instances) and claim that parallel processing of neurons during training improves classification performance. The authors begin with a generic overview of data mining, knowledge discovery in databases (KDD), and the role of classification, then introduce neural networks, emphasizing their ability to model non‑linear relationships, tolerate noisy data, and be parallelized because each neuron’s computation is independent. A brief literature review lists numerous medical applications of ANNs, from imaging to drug discovery, but no direct comparison to other techniques is provided.

In the experimental section the dataset is described: four classes (Normal, First Stroke, Second Stroke, End‑of‑Life) and 13 attributes (age, sex, chest pain type, resting blood pressure, cholesterol, fasting blood sugar, resting ECG, maximum heart rate, exercise‑induced angina, ST depression, slope of ST segment, number of major vessels, thalassemia). All continuous variables are linearly scaled to the