Diabetes and its Complication Prediction using Multi-Task Learning
Shubharthi Dey1, Bagish Choudhury2, S. Sharanya3
1Shubharthi Dey*, Department of Computer Science and Engineering Department, SRM Institute of Science and Technology, Kattankulathur, Chennai, India.
2Bagish Choudhury, Department of Computer Science and Engineering Department, SRM, Institute of Science and Technology, Kattankulathur, Chennai, India.
3S. Sharanya, Department of Computer Science and Engineering Department, SRM Institute of Science and Technology, Kattankulathur, Chennai, India.
Manuscript received on February 10, 2020. | Revised Manuscript received on February 25, 2020. | Manuscript published on March 10, 2020. | PP: 1-3 | Volume-9 Issue-5, March 2020. | Retrieval Number: E2821039520/2020©BEIESP | DOI: 10.35940/ijitee.E2821.039520
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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: Diabetes is a long-term disease that ends up in multiple side-effects. It has now become a reticent exterminator in society because it doesn’t reveal any signs hitherto to the patients until it’s too late. It leads to many complications to other organs, such as kidney, cardiovascular, liver or blood pressure [1]. This work tends to apply a unique multitask learning [2] to synchronously map the relation between manifold complications wherever every task conforms to risks of modelling of complications [3]. It also uses feature selection to reduce the set of risk factors from high-dimensional datasets. Then using the concept of correlation, it finds the degree of relativity among various side effects. The proposed method is able to identify the possible future health hazards identified with the diabetes patient. This will enable us to explain medical conditions and can improves healthcare applications which would help to improve disease prediction performance.
Keywords: Diabetes Risk; Feature Selection, Healthcare, Multitask Learning
Scope of the Article: Deep learning