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A Deep Learning Algorithm for Multiple Disease Prediction Using the IoT and Its Implications
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Author(s): N. Gopinath (SRM institute of Science and Technology, India), S. Vijayakumar (B.S. Abdur Rahman Crescent Institute of Science and Technology, India), P. Tamilarasan (SRM Institute of Science and Technology, India), Maria Pius Pallan (Dr. D.Y. Patil Vidyapeeth, Pune, India)and Swati Dhondiram Jadhav (International Institute of Information Technology, India)
Copyright: 2025
Pages: 28
Source title:
Future Innovations in the Convergence of AI and Internet of Things in Medicine
Source Author(s)/Editor(s): Velliangiri Sarveshwaran (National Chung Cheng University, Taiwan), Karthikeyan Periyaswami (National Chung Cheng University, Taiwan)and Keping Yu (University of Hosei, Japan)
DOI: 10.4018/979-8-3693-7703-1.ch012
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Abstract
Popular culture portrays chronic disease patients as unable to perform daily tasks and needing constant medical attention. Chronic diseases like cardiovascular disease, pneumonia, renal disease, and diabetes cause the most death and disability worldwide. These diseases are difficult to detect with regular clinical data analysis. Predicting chronic diseases earlier could save many lives. Thanks to healthcare IoT, we can monitor, assess, identify, and control many chronic diseases and provide chronic disease prevention methods. New technologies like deep learning are emerging to overcome the internet of things (IoT)'s restrictions on what can be used for the aforementioned purposes. This chapter attempts chronic disease prediction using deep learning. The authors use deep learning, feature augmentation, and convolutional neural networks to predict chronic kidney disease, diabetes, heart disease, and pneumonia. For chronic disease prediction, an integrated model using the algorithms is suggested.
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