Health Diagnosis by using Machine Learning Algorithms
B.Pavitra1, R.Nagaswetha2, E.Sathish3
1B. Pavitra, Department of Electronics and Communication engineering from ANNA University Chennai(TN), India,
2Naga swetha R, Department of Electronics and Communication engineering JNTUH University, Hyderabad, (Telangana), India.
3Sathish Egadhandi, Departmet of in Electronics and Communication engineering from the JNTUH University, Hyderabad, (Telangana), India.
Manuscript received on 23 March 2019 | Revised Manuscript received on 30 March 2019 | Manuscript published on 30 March 2019 | PP: 1707-1711 | Volume-7 Issue-6, March 2019 | Retrieval Number: F2218037619/19©BEIESP
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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 the field of health care, the health monitoring contributes a wide variety of applications such as hospitals, homecare unit, sports training and emergency monitoring systems. In this project, a wireless system is designed for health monitoring. The developed integrated system is used for monitoring of patient’s Pulse rate, Systolic pressure, Diastolic Pressure and Temperature by using machine learning algorithm. By taking Machine learning algorithms such as Logistic regression, SVM, k-NN, Decision Tree and Random Forest , we trained that algorithms for predict the person health condition depending on previous datasheet. Depending on accuracy choose best algorithm and push message alert to mobile app. For wiring the sensors data APIs and Model predicted output Node-Red Tool was used.
Keywords: ML, Logistic regression, SVM, k-NN, Decision Tree.
Scope of the Article: Deep Learning