Authors:
Debmalya Chatterjee
1
and
Saravanan Chandran
2
Affiliations:
1
Software Engineer, GGK Technologies Hyderabad and India
;
2
Department of Computer Science and Engineering, National Institute of Technology Durgapur and India
Keyword(s):
Machine Learning, Heart Disease Prediction, Heart Disease Classification.
Abstract:
Machine Learning (ML) is transforming the industries from delivering normal products to deliver intellect products. Large sets of data points are analysed by the computers and the relationship modelling is applied in a predictive way in real time to obtain accurate results. Machine Learning is adopted in healthcare problems for increasing efficiencies, saving money, and saving lives. The cost of medical treatment is reduced and the healthcare processes are optimized throughout the organization with the support of ML. ML improves healthcare delivery and patient health. Machine learning improves diagnosis and treatment options, also empowers individuals to take control of their health. Diagnosis advancements, predictive healthcare, medicines, and helping patients through ML interface produces better results. Heart Disease relates to many numbers of medical complications related to the heart. In recent years, ML has spread its knowledge in every field. In healthcare, the usage of ML has
been significantly increased. This research work aims at the prediction of heart disease and classification of heart disease using Machine Learning algorithms. The experimental results are classified into five heart disease stages using values 0, 1, 2, 3, and 4, value 0 for no heart disease and 4 for severe heart disease. The Area Under the Curve (AUC) values depict the accuracy level of the prediction using this proposed model. The results are displayed using the data set in the form of charts that is easy to analyse the number of people having chest pains. The ML analytical report added up in the form of charts or other visuals, the results are reported informatively. This analysis is helpful for doctors and the medical industry for several case studies.
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