Machine Learning Based Behavioral Modification

,; Cahyadi, Ani; Razak, Abduk; ,; Abdillah, Husni; ,; Junaedi, Farid; ,; Taligansing, Sri Yunita; ,

doi:10.35940/ijeat.f1299.0886s219

Cited by 3 publications

References 7 publications

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Trends in Using IoT with Machine Learning in Health Prediction System

2021

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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.

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Trends in Using IoT with Machine Learning in Health Prediction System

2021

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Leveraging IoT and Machine Learning for Improved Health Prediction Systems

Thottempudi

2024

Practice, Progress, and Proficiency in Sustainability

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Machine learning (ML) is a powerful tool that unveils hidden insights from internet of things (IoT) data. These technologies enhance decision-making in education, security, business, and healthcare. In healthcare, they automate tasks such as maintaining records, predicting diagnoses, and monitoring patients in real time. However, different ML algorithms perform differently on various datasets, influencing results and clinical decisions. Understanding these ML algorithms and their application in handling IoT data in healthcare is crucial. This chapter highlights key ML algorithms for classification and prediction, providing an in-depth overview of their role in analyzing IoT medical data. The analysis reveals that different ML prediction algorithms have unique limitations, necessitating careful selection based on the dataset type for accurate healthcare predictions. The chapter also illustrates the use of IoT and ML in predicting future healthcare trends.

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Trends in using IoT with machine learning in smart health assessment

Rath

2022

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The Internet of Things (IoT) provides a rich source of information that can be uncovered using machine learning (ML). The decision-making processes in several industries, such as education, security, business, and healthcare, have been aided by these hybrid technologies. For optimum prediction and recommendation systems, ML enhances the Internet of Things (IoT). Machines are already making medical records, diagnosing diseases, and monitoring patients using IoT and ML in the healthcare industry. Various datasets need different ML algorithms to perform well. It's possible that the total findings will be impacted if the predicted results are not consistent. In clinical decision-making, the variability of prediction outcomes is a major consideration. To effectively utilise IoT data in healthcare, it's critical to have a firm grasp of the various machine learning techniques in use. Algorithms for categorization and prediction that have been employed in the healthcare industry are highlighted in this article. As stated earlier, the purpose of this work is to provide readers with an in-depth look at current machine learning algorithms and how they apply to IoT medical data.

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Machine Learning Based Behavioral Modification

Cited by 3 publications

References 7 publications

Trends in Using IoT with Machine Learning in Health Prediction System

Trends in Using IoT with Machine Learning in Health Prediction System

Leveraging IoT and Machine Learning for Improved Health Prediction Systems

Trends in using IoT with machine learning in smart health assessment

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