Development of an explainable machine learning model for Alzheimer’s disease prediction using clinical and behavioural features
This article presents a reproducible machine learning methodology for the early prediction of Alzheimer’s disease (AD) using clinical and behavioural data. A comparative analysis of multiple classification algorithms was conducted, with the Gradient Boosting classifier yielding the best performance...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-12-01
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| Series: | MethodsX |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S221501612500336X |
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| author | Rajkumar Govindarajan K. Thirunadanasikamani Komal Kumar Napa S. Sathya J. Senthil Murugan K. G. Chandi Priya |
| author_facet | Rajkumar Govindarajan K. Thirunadanasikamani Komal Kumar Napa S. Sathya J. Senthil Murugan K. G. Chandi Priya |
| author_sort | Rajkumar Govindarajan |
| collection | DOAJ |
| description | This article presents a reproducible machine learning methodology for the early prediction of Alzheimer’s disease (AD) using clinical and behavioural data. A comparative analysis of multiple classification algorithms was conducted, with the Gradient Boosting classifier yielding the best performance (accuracy: 93.9 %, F1-score: 91.8 %). To improve interpretability, SHapley Additive exPlanations (SHAP) were integrated into the workflow to quantify feature contributions at both global and individual levels. Key predictive variables such as Mini-Mental State Examination (MMSE), Activities of Daily Living (ADL), cholesterol levels, and functional assessment scores were identified and visualized using SHAP-based insights. A user-friendly, interactive web application was developed using Streamlit, allowing real-time patient data input and transparent model output visualization. This method offers a practical tool for clinicians and researchers to support early diagnosis and personalized risk assessment of AD, thus aiding in timely and informed clinical decision-making.Accurate Prediction: Gradient Boosting model achieved 93.9 % accuracy for early Alzheimer’s detection.Explainability: SHAP values provided interpretable insights into key clinical features.Clinical Tool: A Streamlit-based web app enabled real-time, explainable predictions for users. |
| format | Article |
| id | doaj-art-b6907cb0b51c4548b9c0461e59dfe782 |
| institution | Matheson Library |
| issn | 2215-0161 |
| language | English |
| publishDate | 2025-12-01 |
| publisher | Elsevier |
| record_format | Article |
| series | MethodsX |
| spelling | doaj-art-b6907cb0b51c4548b9c0461e59dfe7822025-07-15T04:16:13ZengElsevierMethodsX2215-01612025-12-0115103491Development of an explainable machine learning model for Alzheimer’s disease prediction using clinical and behavioural featuresRajkumar Govindarajan0K. Thirunadanasikamani1Komal Kumar Napa2S. Sathya3J. Senthil Murugan4K. G. Chandi Priya5Department of Computer Science and Engineering, St. Peter’s Institute of Higher Education and Research, Avadi, Chennai, IndiaDepartment of Computer Science and Engineering, St. Peter’s Institute of Higher Education and Research, Avadi, Chennai, IndiaDepartment of Artificial Intelligence and Data Science, Saveetha Engineering College, Chennai, India; Corresponding author.Department of Electronics and Communication Engineering, S. A. Engineering College, Chennai, IndiaDepartment of Computer Science and Engineering, Vel Tech High Tech Dr. Rangarajan Dr. Sakunthala Engineering College, Chennai, IndiaDepartment of Science and Humanities (General Engineering Division), R.M.K. College of Engineering and Technology, Chennai, IndiaThis article presents a reproducible machine learning methodology for the early prediction of Alzheimer’s disease (AD) using clinical and behavioural data. A comparative analysis of multiple classification algorithms was conducted, with the Gradient Boosting classifier yielding the best performance (accuracy: 93.9 %, F1-score: 91.8 %). To improve interpretability, SHapley Additive exPlanations (SHAP) were integrated into the workflow to quantify feature contributions at both global and individual levels. Key predictive variables such as Mini-Mental State Examination (MMSE), Activities of Daily Living (ADL), cholesterol levels, and functional assessment scores were identified and visualized using SHAP-based insights. A user-friendly, interactive web application was developed using Streamlit, allowing real-time patient data input and transparent model output visualization. This method offers a practical tool for clinicians and researchers to support early diagnosis and personalized risk assessment of AD, thus aiding in timely and informed clinical decision-making.Accurate Prediction: Gradient Boosting model achieved 93.9 % accuracy for early Alzheimer’s detection.Explainability: SHAP values provided interpretable insights into key clinical features.Clinical Tool: A Streamlit-based web app enabled real-time, explainable predictions for users.http://www.sciencedirect.com/science/article/pii/S221501612500336XAlzheimer's disease predictionExplainable artificial intelligenceSHAP valuesGradient boosting classifierStreamlit web application |
| spellingShingle | Rajkumar Govindarajan K. Thirunadanasikamani Komal Kumar Napa S. Sathya J. Senthil Murugan K. G. Chandi Priya Development of an explainable machine learning model for Alzheimer’s disease prediction using clinical and behavioural features MethodsX Alzheimer's disease prediction Explainable artificial intelligence SHAP values Gradient boosting classifier Streamlit web application |
| title | Development of an explainable machine learning model for Alzheimer’s disease prediction using clinical and behavioural features |
| title_full | Development of an explainable machine learning model for Alzheimer’s disease prediction using clinical and behavioural features |
| title_fullStr | Development of an explainable machine learning model for Alzheimer’s disease prediction using clinical and behavioural features |
| title_full_unstemmed | Development of an explainable machine learning model for Alzheimer’s disease prediction using clinical and behavioural features |
| title_short | Development of an explainable machine learning model for Alzheimer’s disease prediction using clinical and behavioural features |
| title_sort | development of an explainable machine learning model for alzheimer s disease prediction using clinical and behavioural features |
| topic | Alzheimer's disease prediction Explainable artificial intelligence SHAP values Gradient boosting classifier Streamlit web application |
| url | http://www.sciencedirect.com/science/article/pii/S221501612500336X |
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