Implementation of Adaptive Technology for Heart Disease Prediction Using IoT and Machine Learning

Authors

  • S. Kavyashree Assistant Professor, Department of Electrical and Electronics Engineering, Vidya Vikas Institute of Engineering and Technology, Mysore, India
  • T. S. Mohith Student, Department of Electrical and Electronics Engineering, Vidya Vikas Institute of Engineering and Technology, Mysore, India
  • B. M. Niranjan Gowda Student, Department of Electrical and Electronics Engineering, Vidya Vikas Institute of Engineering and Technology, Mysore, India
  • M. Ananya Student, Department of Electrical and Electronics Engineering, Vidya Vikas Institute of Engineering and Technology, Mysore, India
  • E. Sinchana Student, Department of Electrical and Electronics Engineering, Vidya Vikas Institute of Engineering and Technology, Mysore, India

Keywords:

Cardiovascular failure, IoT, UCI respiratory dataset, CNN and KNN, Precision

Abstract

Most recent progressions in field of IoT and detecting innovations can be utilized for Heart Attacks administrations. The tremendous amount of data is being shaped through the IoT gadgets in the clinical field and distributed computing procedures have been utilized to deal with the huge measure of information. To benefit great support of the client utilizing the online Heart Attacks, a new Cloud just as IoT based Healthcare application to screen notwithstanding analyze genuine illnesses is created. In this examination, an effective structure is used for coronary illness is made using the UCI Repository dataset just as the medical care sensors to anticipate the public who experience the ill effects of coronary illness. In addition, grouping calculations are utilized to order the patient information for the recognizable proof of coronary illness. In the preparation stage, the classifier will be prepared utilizing the information from benchmark dataset. During the testing stage, the real tolerant information to distinguish sickness is utilized to recognize the presence of infection. For experimentation, a benchmark dataset is tried utilizing a bunch of classifiers to be specific J48, CNN and KNN. The reproduction results guaranteed that the J48 classifiers shows unrivaled execution as far as various measures like exactness, accuracy, review, F-score and kappa esteem.

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Published

2021-07-11

How to Cite

[1]
S. Kavyashree, T. S. Mohith, B. M. N. Gowda, M. Ananya, and E. Sinchana, “Implementation of Adaptive Technology for Heart Disease Prediction Using IoT and Machine Learning”, IJRESM, vol. 4, no. 7, pp. 94–96, Jul. 2021.

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Articles