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Erschienen in:
2021 | OriginalPaper | Buchkapitel
Diagnosis of Heart Disease Using Internet of Things and Machine Learning Algorithms
verfasst von : Amit Kishor, Wilson Jeberson
Erschienen in: Proceedings of Second International Conference on Computing, Communications, and Cyber-Security
Verlag: Springer Singapore
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Abstract
In the current scenario of the digital world, the healthcare industry generates a huge amount of patient data. Manual handling of these produced data becomes very difficult for doctors. The Internet of things (IoT) is very effectively handling the produced data. The IoT captured huge amounts of data, and with the machine learning algorithms, it can detect the disease and diagnose the disease. The work aims to apply various machine learning methods to the produced data. A machine learning framework has proposed for early prediction of heart disease in conjunction with IoT. The developed model is evaluated with k-nearest neighbor (K-NN), decision trees (DTs), random forest (RF), multilayer perceptron (MLP), Naïve Bayes (NB), and linear-support vector machine (L-SVM). The model achieved the diagnostic accuracy for 82.4%, 81.3%, 92.3%, 88.2%, 89.6%, and 82.4% for K-NN, DT, RF, MLP, NB, and L-SVM, respectively. As per the experimental results, random forest has the highest prognosis rate of 92.3%.