Review of Techniques used to find the Possibility of Getting Heart Related Disease
Amandeep Singh1, Amit Chhabra2
1Amandeep Singh, Department of Computer Science and Engineering, GNDU, Amritsar, Punjab, India.
2Amit Chhabra, Assistant Professor, Department of Computer Science and Engineering, GNDU, Amritsar, Punjab, India.
Manuscript received on July 14, 2020. | Revised Manuscript received on July 28, 2020. | Manuscript published on August 10, 2020. | PP: 250-253 | Volume-9 Issue-10, August 2020 | Retrieval Number: 100.1/ijitee.J74410891020 | DOI: 10.35940/ijitee.J7441.0891020
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: As with the changing lifestyle and people consuming high calorie diet increases the heart disease rate among the humans. Over the last decade heart related diseases are one of the leading cause of death cases every year. It is very hard to notice the symptoms of any heart related disease at early stage and in many cases it leads to sudden death before ever knowing the first symptom of any heart related problem. With the advancement of technology there are many devices which are used to perform several tests in the medical field and with the emerging trend of Machine learning doctors can be aided to find symptoms of heart disease . There is huge amount of patients health data collected by healthcare institutes which can be used for data mining and infer relationship between data and helps in predicting heart diseases. The machine learning models trained on patients record data which shows symptoms is used to predict the probability for having a heart disease.
Keywords: Machine learning, Supervised learning, Unsupervised learning, Heart disease.
Scope of the Article: Machine learning