Statistic Solution for Machine Learning to Analyze Heart Disease Data

Data crawling, collection and analysis have become a popular pillar for the business intelligence of big data analysis which is the latest hot-topic among the research association. Numerous tools and techniques to solve and analyze the structured and unstructured datasets are developing very quickly. The previous studies show the different approaches in the identification of the strengths and weaknesses of multiple machine learning algorithms. But, most of the approaches demand more expert knowledge base information to understand the concepts of given data. In this paper, we modernize the machine learning methods for the effective prediction of heart disease. This work deliberates the detailed process of implementation of our proposed system. The goal of this work is to find a strong and effective machine learning algorithm for disease prediction for the problem; how can doctors get fast and better results for their diagnosis of heart disease. We design a new system for disease prediction using machine learning prediction algorithms (LR, ANN and SVC) by utilizing an effective approach of ETL, OLAP and data mining. The results showed that the best machine learning algorithm is SVC with 92% accuracy for the risk prediction model. We found that subjects at 56-64 years old have a high risk of heart disease, as well as men, have more heart disease rate than women. This proposed study can be favorable for the medical practitioners in the field of healthcare, supportive practice and precautions to the heart disease patients.