dorsal/arxiv
View SchemaPredictive Analytics for Dementia: Machine Learning on Healthcare Data
| Authors | Shafiul Ajam Opee, Nafiz Fahad, Anik Sen, Rasel Ahmed, Fariha Jahan, Md. Kishor Morol, Md Rashedul Islam |
|---|---|
| Categories | |
| ArXiv ID | 2601.07685vv1 |
| URL | https://arxiv.org/abs/2601.07685 |
| License | http://creativecommons.org/licenses/by-nc-nd/4.0/ |
Abstract
Dementia is a complex syndrome impacting cognitive and emotional functions, with Alzheimer's disease being the most common form. This study focuses on enhancing dementia prediction using machine learning (ML) techniques on patient health data. Supervised learning algorithms are applied in this study, including K-Nearest Neighbors (KNN), Quadratic Discriminant Analysis (QDA), Linear Discriminant Analysis (LDA), and Gaussian Process Classifiers. To address class imbalance and improve model performance, techniques such as Synthetic Minority Over-sampling Technique (SMOTE) and Term Frequency-Inverse Document Frequency (TF-IDF) vectorization were employed. Among the models, LDA achieved the highest testing accuracy of 98%. This study highlights the importance of model interpretability and the correlation of dementia with features such as the presence of the APOE-epsilon4 allele and chronic conditions like diabetes. This research advocates for future ML innovations, particularly in integrating explainable AI approaches, to further improve predictive capabilities in dementia care.
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"abstract": "Dementia is a complex syndrome impacting cognitive and emotional functions, with Alzheimer\u0027s disease being the most common form. This study focuses on enhancing dementia prediction using machine learning (ML) techniques on patient health data. Supervised learning algorithms are applied in this study, including K-Nearest Neighbors (KNN), Quadratic Discriminant Analysis (QDA), Linear Discriminant Analysis (LDA), and Gaussian Process Classifiers. To address class imbalance and improve model performance, techniques such as Synthetic Minority Over-sampling Technique (SMOTE) and Term Frequency-Inverse Document Frequency (TF-IDF) vectorization were employed. Among the models, LDA achieved the highest testing accuracy of 98%. This study highlights the importance of model interpretability and the correlation of dementia with features such as the presence of the APOE-epsilon4 allele and chronic conditions like diabetes. This research advocates for future ML innovations, particularly in integrating explainable AI approaches, to further improve predictive capabilities in dementia care.",
"arxiv_id": "2601.07685",
"authors": [
"Shafiul Ajam Opee",
"Nafiz Fahad",
"Anik Sen",
"Rasel Ahmed",
"Fariha Jahan",
"Md. Kishor Morol",
"Md Rashedul Islam"
],
"categories": [
"cs.AI"
],
"license": "http://creativecommons.org/licenses/by-nc-nd/4.0/",
"title": "Predictive Analytics for Dementia: Machine Learning on Healthcare Data",
"url": "https://arxiv.org/abs/2601.07685",
"version": "v1"
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