Exploring the realm of machine learning involves monitoring the health of elderly loved ones by tracking their motions to keep them healthy. Datasets created by recording the body movements of elderly people are input to machine learning models for prediction. In this study, the proposal is to compare two popular machine learning algorithms KNN and K-Means for parameters like accuracy and precision. The ageing population has become a significant concern worldwide, as it poses a significant challenge to healthcare systems. The deterioration of health in elderly individuals is multifactorial, and it is essential to develop predictive models to identify potential health risks and intervene early. This study aims to explore using KNN(K-Nearest Neighbours) and K Means algorithms to analyse the health data of elderly individuals. The study collected and analyzed data from a cohort of elderly individuals, including demographic, lifestyle, and clinical parameters. The KNN algorithm was used to predict the likelihood of developing chronic diseases, such as diabetes, hypertension, and cardiovascular diseases, based on the input features.
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Taschenbuch. Etat : Neu. This item is printed on demand - Print on Demand Titel. Neuware -Exploring the realm of machine learning involves monitoring the health of elderly loved ones by tracking their motions to keep them healthy. Datasets created by recording the body movements of elderly people are input to machine learning models for prediction. In this study, the proposal is to compare two popular machine learning algorithms KNN and K-Means for parameters like accuracy and precision. The ageing population has become a significant concern worldwide, as it poses a significant challenge to healthcare systems. The deterioration of health in elderly individuals is multifactorial, and it is essential to develop predictive models to identify potential health risks and intervene early. This study aims to explore using KNN(K-Nearest Neighbours) and K Means algorithms to analyse the health data of elderly individuals. The study collected and analyzed data from a cohort of elderly individuals, including demographic, lifestyle, and clinical parameters. The KNN algorithm was used to predict the likelihood of developing chronic diseases, such as diabetes, hypertension, and cardiovascular diseases, based on the input features.VDM Verlag, Dudweiler Landstraße 99, 66123 Saarbrücken 68 pp. Englisch. N° de réf. du vendeur 9786208116040
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Taschenbuch. Etat : Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Exploring the realm of machine learning involves monitoring the health of elderly loved ones by tracking their motions to keep them healthy. Datasets created by recording the body movements of elderly people are input to machine learning models for prediction. In this study, the proposal is to compare two popular machine learning algorithms KNN and K-Means for parameters like accuracy and precision. The ageing population has become a significant concern worldwide, as it poses a significant challenge to healthcare systems. The deterioration of health in elderly individuals is multifactorial, and it is essential to develop predictive models to identify potential health risks and intervene early. This study aims to explore using KNN(K-Nearest Neighbours) and K Means algorithms to analyse the health data of elderly individuals. The study collected and analyzed data from a cohort of elderly individuals, including demographic, lifestyle, and clinical parameters. The KNN algorithm was used to predict the likelihood of developing chronic diseases, such as diabetes, hypertension, and cardiovascular diseases, based on the input features. N° de réf. du vendeur 9786208116040
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Taschenbuch. Etat : Neu. Analyzing Machine Learning Algorithms: KNN & K-Means for Elderly Heal | Vanshika Walia (u. a.) | Taschenbuch | Englisch | 2024 | LAP LAMBERT Academic Publishing | EAN 9786208116040 | Verantwortliche Person für die EU: preigu GmbH & Co. KG, Lengericher Landstr. 19, 49078 Osnabrück, mail[at]preigu[dot]de | Anbieter: preigu. N° de réf. du vendeur 130161993
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