A Survey on Heart Disease Forecasting using Hybrid Technique in Data Mining

Abstract: Data mining is a process of extracting information and discovering useful patterns from the vast amount of data. It is also known as Knowledge Discovery from Data (KDD).The healthcare industry gathers very large amounts of healthcare data which leads to the need for powerful data analysis tools to extract useful information. Disease diagnosis is one of the applications where data mining techniques are showing successful results. Researchers are working on several statistical analysis and data mining techniques to enhance the disease diagnosis accuracy in medical healthcare. Heart disease diagnosis is evaluated as the complex tasks in the medical field. Every year the number of death is increasing because of heart disease. Different data mining techniques individually give low accuracy for disease diagnosis. Researchers are examining the effect of combining more than one technique showing enhanced results in the diagnosis of heart disease. In this paper, we survey different papers in which single techniques or combination of different data mining techniques are used to the forecasting of heart disease so that we can recognize the technique with high accuracy for future research.

Keywords: Data mining; Heart disease forecasting; Data mining techniques.