Machine Learning Algorithm for Early Detection of Heart Diseases Using 3 Tier IoT Architecture
Y. Madan Reddy1, B. Pavani2
1Y.Madan Reddy, Assistant Professor, Department of CSE, Malla Reddy Engineering College for Women, Hyderabad (Telangana), India.
2B.Pavani, Assistant Professor, Department of CSE, Malla Reddy College of Engineering & Technology, Hyderabad (Telangana), India.
Manuscript received on 01 November 2019 | Revised Manuscript received on 13 November 2019 | Manuscript Published on 22 November 2019 | PP: 1877-1882 | Volume-8 Issue-6S3 September 2019 | Retrieval Number: F13600986S319/19©BEIESP | DOI: 10.35940/ijeat.F1360.0986S319
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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: Among the applications empowered by the Internet of Things (IoT), regular health monitoring framework is an important one. Wearable sensor gadgets utilized in IoT health monitoring framework have been producing huge amount of data on regular basis. The speed of data generation by IoT sensor gadgets is very high. Henceforth, the volume of data generated from the IoT-based health monitoring framework is also very high. So as to overcome this problem, this paper proposes adaptable three-tier architecture to store and process such immense volume of wearable sensor data. Tier 1 focuses on gathering of data from IoT wearable sensor gadgets. Tier 2 employs Apache HBase for storing substantial volume of wearable IoT sensor data in cloud computing. Likewise, Tier-3 utilizes Apache Mahout for building up logistic regression-based prediction model for heart related issues. At long last, ROC examination is performed to identify the most significant clinical parameters to get heart diseases.
Keywords: Machine Learning Algorithm IoT Architecture Monitoring Framework.
Scope of the Article: Machine Learning