Loading...
Dr. Sam Sangeeth G. is affiliated with the Department of Social Work at MATS University, Raipur, Chhattisgarh, India. His academic affiliation connects his work with social work education and related scholarly activities. His professional role contributes to higher education. His chapter contribution concerns AI-driven approaches to Alzheimer's disease prediction and cognitive decline analysis.
Alzheimer's disease is a progressive neurological condition marked by a decline in memory, cognition, behaviour, and the capacity to carry out daily tasks. Prompt recognition of at-risk persons and thorough examination of cognitive deterioration are crucial for prompt clinical evaluation, suitable care strategising, and enhanced disease oversight. Machine learning (ML) methodologies provide avenues to examine these varied information sources and discern patterns linked to Alzheimer’s disease, mild cognitive impairment, and progressive alterations in cognitive performance. This chapter offers an extensive summary of machine learning-driven predictions for Alzheimer’s disease and the analysis of cognitive deterioration, encompassing clinical data, neuropsychological evaluations, magnetic resonance imaging (MRI), positron emission tomography (PET), genetic data, and longitudinal patient records. The section also explores difficulties related to data variability, absent data points, class disproportion, model transparency, privacy concerns, equity, and clinical verification. The suggested methodology highlights the need of reproducible experimentation and the conscientious incorporation into healthcare processes. Machine learning need to be viewed as an adjunct to clinical decision-making rather than a substitute for thorough medical assessment.
Parkinson's disease (PD) is a progressive neurodegenerative condition marked by motor symptoms such tremors, rigidity, bradykinesia, and postural instability, with non-motor issues including sleep problems, cognitive decline, depression, and autonomic dysfunction. Timely identification and accurate evaluation of disease advancement are crucial for facilitating clinical assessment, therapeutic strategy formulation, and ongoing patient surveillance. This section explores AI-based methodologies for identifying Parkinson’s disease, evaluating symptom intensity, and forecasting disease advancement. It offers a conceptual structure that amalgamates multimodal data preprocessing, feature extraction, feature selection, classification, longitudinal modelling, and interpretable AI. Supervised learning algorithms, ensemble techniques, convolutional neural networks, recurrent neural networks, and time-series models are examined concerning various data modalities. Evaluation methodologies for categorisation, calibration, clinical applicability, and longitudinal forecasting are likewise delineated. Ethical factors, safeguarding privacy, clarity of interpretation, and clinical verification are underscored. The suggested approach aims to facilitate research on AI-enhanced evaluation of Parkinson's disease, rather than supplant neurological assessments or expert medical opinion.