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2024 | OriginalPaper | Buchkapitel

Qualitative Research Reasoning on Dementia Forecast Using Machine Learning Techniques

verfasst von : Tanvi Kapdi, Apurva Shah

Erschienen in: Advances in Data-Driven Computing and Intelligent Systems

Verlag: Springer Nature Singapore

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Abstract

The rise in mental health issues and the demand for high-quality medical care have prompted researchers to investigate how machine learning might be used to treat mental health issues. Dementia is a disease that causes loss of cognitive skills in a way that interferes with a person’s day-to-day activities. It causes a breakdown of brain function, comprehension, recognition, reasoning, and behavioral abilities to the point where a person experiences difficulties in day-to-day activities. Dementia gradually kills the brain cells and causes people to lose their reading and thinking capabilities. According to the Lancet report, the incidences of dementia cases in India are predicted to nearly triple by 2050. According to the survey, the number of cases is roughly predicted to quadruple to 153 million by 2050. This research presents the analysis and findings related to forecasting dementia using machine learning techniques. The study has been conducted using the Open Access Series of Imaging Studies (OASIS) dataset. This dataset has been explored by using various machine learning algorithms such as support vector machine, random forest, decision tree, logistic regression, AdaBoost, and XGBoost. The conclusion has been drawn regarding the evaluation metrics in accuracy. It has been found that XGBoost gave the best result with 93.02% accuracy. With XGBoost, it is simple to determine the ideal number of boosting iterations in a single run.

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Titel: Qualitative Research Reasoning on Dementia Forecast Using Machine Learning Techniques
verfasst von: Tanvi Kapdi
Apurva Shah
Verlag: Springer Nature Singapore
Buch: Advances in Data-Driven Computing and Intelligent Systems
Print ISBN: 978-981-9995-20-2

Electronic ISBN: 978-981-9995-21-9

Copyright-Jahr: 2024
DOI: https://doi.org/10.1007/978-981-99-9521-9_9

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