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Remote Health Prediction System: A Machine Learning-Based Approach

Remote Health Prediction System: A Machine Learning-Based Approach

Pardhu Thottempudi, Nagesh Deevi, Amy Prasanna T., Srinivasarao N., Mahesh Babu Katta
Copyright: © 2023 |Pages: 12
ISBN13: 9781668444665|ISBN10: 1668444666|ISBN13 Softcover: 9781668444672|EISBN13: 9781668444689
DOI: 10.4018/978-1-6684-4466-5.ch010
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MLA

Thottempudi, Pardhu, et al. "Remote Health Prediction System: A Machine Learning-Based Approach." Multi-Disciplinary Applications of Fog Computing: Responsiveness in Real-Time, edited by Debi Prasanna Acharjya and Kauser Ahmed P., IGI Global, 2023, pp. 195-206. https://doi.org/10.4018/978-1-6684-4466-5.ch010

APA

Thottempudi, P., Deevi, N., T., A. P., N., S., & Katta, M. B. (2023). Remote Health Prediction System: A Machine Learning-Based Approach. In D. Acharjya & K. P. (Eds.), Multi-Disciplinary Applications of Fog Computing: Responsiveness in Real-Time (pp. 195-206). IGI Global. https://doi.org/10.4018/978-1-6684-4466-5.ch010

Chicago

Thottempudi, Pardhu, et al. "Remote Health Prediction System: A Machine Learning-Based Approach." In Multi-Disciplinary Applications of Fog Computing: Responsiveness in Real-Time, edited by Debi Prasanna Acharjya and Kauser Ahmed P., 195-206. Hershey, PA: IGI Global, 2023. https://doi.org/10.4018/978-1-6684-4466-5.ch010

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Abstract

One of the many applications of machine learning in healthcare is the analysis of large amounts of data to reveal new therapeutic insights. Once doctors have this data, they can better serve their patients. Therefore, satisfaction can be raised by using deep learning to enhance the quality of care provided. This work aims to integrate machine learning and AI in healthcare into a single system. Predictive algorithms based on machine learning could revolutionize healthcare by allowing doctors to avoid unnecessary treatments. Various libraries, including those for machine learning algorithms, were used to develop this work. Because of its extensive library and user-friendliness, Python has emerged as the preferred language. syntax. The authors used various classification techniques to train machine learning models and then select the one that provided the best balance between accuracy and precision while avoiding prediction error and autocorrelation problems, the two main causes of bias and variance.

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