UNVEILING NOVEL PREDICTORS OF ALZHEIMER’S DISEASE: A FUNCTIONAL AND BEHAVIORAL-BASED CLUSTERING APPROACH

Alzheimer’s disease (AD) is a progressive neurodegenerative disorder traditionally diagnosed using cognitive assessments, which may overlook critical functional and behavioral symptoms. This study employs statistical and machine learning techniques to analyze a publicly available Kaggle dataset of Alzheimer’s patients, identifying novel predictors of disease progression. Ethical considerations were addressed by adhering to secondary data analysis guidelines, with feature selection performed using correlation analysis and principal component analysis (PCA). Model validation, including 10-fold cross-validation for logistic regression and silhouette analysis for clustering, ensured robust results. Our findings reveal that functional impairment and behavioral symptoms are stronger predictors of AD than cognitive scores alone. Logistic regression analysis demonstrated that memory complaints and behavioral symptoms had the highest predictive significance (p < 0.0001), while Mini-Mental State Examination (MMSE) scores showed weaker correlation with diagnosis. Cluster analysis identified three distinct patient subgroups: behavioral symptom-dominant, memory complaint-dominant, and silent decline patients, who exhibit functional impairment without self-reported cognitive deficits. The silent decline subgroup highlights a critical gap in conventional screening methods, where patients may go undiagnosed until significant disease progression occurs. Despite these insights, the study acknowledges limitations in the dataset, including potential demographic biases, missing contextual information, and reliance on self-reported measures. These limitations underscore the need for future research to incorporate diverse datasets, longitudinal studies, and objective measures such as biomarkers. This study advocates for a paradigm shift in AD diagnosis, integrating machine learning-driven models that analyze functional and behavioral symptoms alongside cognitive assessments. By promoting multidimensional diagnostic frameworks, this research aims to enhance early detection, personalize treatment approaches, and improve patient outcomes in Alzheimer’s disease management.Alzheimer’s disease (AD) is a progressive neurodegenerative disorder traditionally diagnosed using cognitive assessments, which may overlook critical functional and behavioral symptoms. This study employs statistical and machine learning techniques to analyze a publicly available Kaggle dataset of Alzheimer’s patients, identifying novel predictors of disease progression. Ethical considerations were addressed by adhering to secondary data analysis guidelines, with feature selection performed using correlation analysis and principal component analysis (PCA). Model validation, including 10-fold cross-validation for logistic regression and silhouette analysis for clustering, ensured robust results. Our findings reveal that functional impairment and behavioral symptoms are stronger predictors of AD than cognitive scores alone. Logistic regression analysis demonstrated that memory complaints and behavioral symptoms had the highest predictive significance (p < 0.0001), while Mini-Mental State Examination (MMSE) scores showed weaker correlation with diagnosis. Cluster analysis identified three distinct patient subgroups: behavioral symptom-dominant, memory complaint-dominant, and silent decline patients, who exhibit functional impairment without self-reported cognitive deficits. The silent decline subgroup highlights a critical gap in conventional screening methods, where patients may go undiagnosed until significant disease progression occurs. Despite these insights, the study acknowledges limitations in the dataset, including potential demographic biases, missing contextual information, and reliance on self-reported measures. These limitations underscore the need for future research to incorporate diverse datasets, longitudinal studies, and objective measures such as biomarkers. This study advocates for a paradigm shift in AD diagnosis, integrating machine learning-driven models that analyze functional and behavioral symptoms alongside cognitive assessments. By promoting multidimensional diagnostic frameworks, this research aims to enhance early detection, personalize treatment approaches, and improve patient outcomes in Alzheimer’s disease management.