Novel Two Level Classification (21-C) Model For Heart Disease Prediction

Heart disease is currently prevailing disease leading to the considerable amount of death cases every year. For few decades, the prediction of heart attack has been a challenge despite the recent advancement in medical science. Recently developed modern technology is widely used in the implementation of non-invasive qualitative and quantitative analysis of the Heart anatomy. Computational and storage techniques and tools for machine learning can be employed to assist physicians in getting diagnosed and predicting disease so that they may provide the appropriate care and avoid the consequences, including death. Predicting the results of a disease is among the most exciting and difficult tasks for which data mining applications can be developed. To develop a novel and enhanced ML model for the prior diagnosis and detection of coronary diseases with high precision and performance metrics, however, a novel enhanced machine learning model called Two Level Classification (2L-C) model for classification of heart disease and prediction using novel Improved Genetic Algorithm (I-GA) is introduced. The proposed work focuses on the classification of cardiovascular disease using following different stages. In first stage, the data Preprocessing step is carried out to reduce the noise in the raw data. The proposed two-level classification (2L-C) model is implemented in second stage. Finally, the improved genetic algorithm is used to search for the most effective variety of risk variables for each input, as well as improvements in control parameters like as learning rate and weight. Experimental result shows the performance improvements in terms of precision of the prediction.