PHtNN: Prediction of Heart Disease Risk Using Twin Neural Network

Cardiovascular disease is one of the primary causes of increasing mortality rates globally, spanning various types of ailments. Aside from a healthy lifestyle, prediction, prognostication, and early diagnosis can all contribute to lower mortality rates. The massive variation in the economic growth and development of countries worldwide, accompanied by the irregular availability of medical experts and radiologists, is a major impediment to early diagnosis. Researchers are working on prediction systems that will aid doctors and radiologists in prognostication and assessment by providing diagnostics to the human race without regard to geographical, economic, or financial inequalities. The use of a computational intelligence-based medical imaging prediction system to either prognosticate or detect and further diagnose the disease is becoming more popular. In this work, a computational intelligence-based prediction system, PHtNN , for heart disease diagnosis has been proposed. PHtNN uses the multiple factor analysis (MFA) to extract features from the heart disease multi-datasets, VA Long Beach, Switzerland, Hungarian, Cleveland, and Z-Alizadeh Sani, and train the model by using twin neural network. The system, PHtNN , is validated using the hold-out validation scheme with a ratio of 3:1. Experimental results reveal that PHtNN outperforms several previous baseline approaches in terms of accuracy and improves the system's efficiency; as a result, it can assist medical experts in diagnosing cardiac patients.