Machine Learning for Healthcare Diagnostics
Tata Sutabri1, R. Pandi Selvam2, K. Shankar3, Phong Thanh Nguyen4, Wahidah Hashim5, Andino Maseleno6
1Tata Sutabri, Universitas Respati Indonesia, Indonesia.
2R. Pandi Selvam, Assistant Professor & Head, PG Department of Computer Science, Ananda College, Devakottai, India.
3K. Shankar, Department of Computer Applications, Alagappa University, Karaikudi, India.
4Phong Thanh Nguyen, Department of Project Management, Ho Chi Minh City Open University, Vietnam.
5Wahidah Hashim, Institute of Informatics and Computing Energy, Universiti Tenaga Nasional, Malaysia.
6Andino Maseleno, Institute of Informatics and Computing Energy, Universiti Tenaga Nasional, Malaysia.
Manuscript received on 15 September 2019 | Revised Manuscript received on 24 September 2019 | Manuscript Published on 10 October 2019 | PP: 999-1001 | Volume-8 Issue-6S2, August 2019 | Retrieval Number: F13040886S219/19©BEIESP | DOI: 10.35940/ijeat.F1304.0886S219
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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 (

Abstract: Presently machine learning and artificial intelligence is playing one of the most important role in diagnose many genetic and non genetic disease. So that the rapid inventions in machine learning can save thousands of life’s as it can diagnose the early stage of many serious diseases. In this research the datasets for such diseases is studied and it will be analyzed that how such deep machine learning will impact to a human life. The problem with such methodology is that it is not possible to get accurate results in the initial stage of research. The reason is every human have different immunity power and stamina. There are many diagnostics center who are fully dependent on the equipments which are fully based on machine learning. In order to boost this process it is necessary to collect the real time patient’s data from different hospitals, states and countries. So that it will be beneficial for world wide.
Keywords: Health Sevices, Treatment Recommender, Complementary Tools.
Scope of the Article: Machine Learning