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| Main Authors: | , , , , , , |
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| Format: | Preprint |
| Published: |
2026
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2601.07685 |
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| _version_ | 1866909987817652224 |
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| author | Opee, Shafiul Ajam Fahad, Nafiz Sen, Anik Ahmed, Rasel Jahan, Fariha Morol, Md. Kishor Islam, Md Rashedul |
| author_facet | Opee, Shafiul Ajam Fahad, Nafiz Sen, Anik Ahmed, Rasel Jahan, Fariha Morol, Md. Kishor Islam, Md Rashedul |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2601_07685 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Predictive Analytics for Dementia: Machine Learning on Healthcare Data Opee, Shafiul Ajam Fahad, Nafiz Sen, Anik Ahmed, Rasel Jahan, Fariha Morol, Md. Kishor Islam, Md Rashedul Artificial Intelligence 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. |
| title | Predictive Analytics for Dementia: Machine Learning on Healthcare Data |
| topic | Artificial Intelligence |
| url | https://arxiv.org/abs/2601.07685 |