Supervised Linear Estimator Modeling (SLEMH) for Health Monitoring
Amandeep Kaur1, Anuj Kumar Gupta2
1Amandeep Kaur, Research Scholar ,Deptt of Computer Science & Engg Ikgptu Kapurthala, Punjab, India.
2Anuj Kumar Gupta, Professor, Deptt of Computer Science & Engg, CGC, LANDRAN, Punjab, India.
Manuscript received on September 22, 2019. | Revised Manuscript received on October 20, 2019. | Manuscript published on October 30, 2019. | PP: 2876-2882 | Volume-9 Issue-1, October 2019 | Retrieval Number: A1133109119/2019©BEIESP | DOI: 10.35940/ijeat.A1133.109119
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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 this research work, the E-Health monitoring system has been developed using fifteen health indicators. These fifteen features were selected by following a Recursive Feature Elimination with Cross-Validation method. The dataset was labeled as per medical limits and segregated into three classes (normal, borderline and onset of unhealthy state). A rigorous process was followed at each step to find out which linear estimator and model is suitable for classifying health condition of persons. Five regression estimators were evaluated and it was found that logistic regression and linear discriminant analysis methods are providing highest accuracy and lowest error for classifying three health states of a patient.