Abstract
Machine learning (ML) is a powerful tool that delivers insights hidden in Internet of Things (IoT) data. These hybrid technologies work smartly to improve the decision-making process in different areas such as education, security, business, and the healthcare industry. ML empowers the IoT to demystify hidden patterns in bulk data for optimal prediction and recommendation systems. Healthcare has embraced IoT and ML so that automated machines make medical records, predict disease diagnoses, and, most importantly, conduct real-time monitoring of patients. Individual ML algorithms perform differently on different datasets. Due to the predictive results varying, this might impact the overall results. The variation in prediction results looms large in the clinical decision-making process. Therefore, it is essential to understand the different ML algorithms used to handle IoT data in the healthcare sector. This article highlights well-known ML algorithms for classification and prediction and demonstrates how they have been used in the healthcare sector. The aim of this paper is to present a comprehensive overview of existing ML approaches and their application in IoT medical data. In a thorough analysis, we observe that different ML prediction algorithms have various shortcomings. Depending on the type of IoT dataset, we need to choose an optimal method to predict critical healthcare data. The paper also provides some examples of IoT and machine learning to predict future healthcare system trends.
Highlights
We find that K-Nearest Neighbor (KNN) may be the most popular algorithm for classification and prediction task
The time-consuming to develop the K-neural network (NN) model, but the set predictions can be very slow in value of x3 lies inare themany rangefeatures of 1‒100.orThe effect involved of x1 andin x2the on learning any distance function, training if there samples process
Low-intensity decision trees are valuable; they may lead to certain vote is obtained in the case of classification, and the average value is obtained when it drawbacks for the model, including low variance and high partiality, while the opposite comes to regression tasks
Summary
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. The IoT application has made it possible for hospitals to monitor the vital signs of patients with chronic conditions [11,12] The system uses such information to predict patient health status in different ways. The IoT offers systems for supervising and monitoring patients via sensor networks made up of both software and hardware The latter includes appliances such as the Raspberry Pi board, blood pressure sensors, temperature sensors, and heart rate sensors. Various open-source cloud computing platforms are compatible with the Raspbian Jessi and Raspberry Pi board [18] These devices utilize machine learning algorithms to assess the stored data to recognize the existence of any anomalies [19]. IoT and machine learning systems to train a system by applying simple data for predicting medical anomalies.
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