Logistic Regression for Health Profiling
Ambika P1, E. Laxmi Lydia2, K. Shankar3, Phong Thanh Nguyen4, Satria Abadi5
1Ambika P, Department of Computer Science, Kristu Jayanti College, Bangalore (Karnataka), India.
2E. Laxmi Lydia, Professor, Department of Computer Science and Engineering, Vignan’s Institute of Information Technology (A), Visakhapatnam (Andhra Pradesh), India.
3K. Shankar, Department of Computer Applications, Alagappa University, Karaikudi (Tamil Nadu), India.
4Phong Thanh Nguyen, Department of Project Management, Ho Chi Minh City Open University, Vietnam.
5Satria Abadi, Department of Information Systems, STMIK Pringsewu, Lampung, Indonesia.
Manuscript received on 15 September 2019 | Revised Manuscript received on 24 September 2019 | Manuscript Published on 10 October 2019 | PP: 974-977 | Volume-8 Issue-6S2, August 2019 | Retrieval Number: F12940886S219/19©BEIESP | DOI: 10.35940/ijeat.F1294.0886S219
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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 (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: In an event when there is lots of risk factor then the logistic regression is used for predicting the probability. For binary and ordinal data the medical researcher increase the use of logistic analysis. Several classification problems like spam detection used logistic regression. If a customer purchases a specific product in Diabetes prediction or they will inspire with any other competitor, whether customer click on given advertisement link or not are some example. For two class classification the Logistic Regression is one of the most simple and common machine Learning algorithms. For any binary classification problem it is very easy to use as a basic approach. Deep learning is also its fundamental concept. The relationship measurement and description between dependent binary variable and independent variables can be done by logistic regression.
Keywords: Logictic, Regression, Medical, Binary Variable.
Scope of the Article: Regression and Prediction