Development of Machine Learning Model for Detection and Diagnosis of Alzheimer's disease. A Comprehensive Review

New techniques of detection and diagnosis are needed for Alzheimer's disease (AD) is a major concern for society's health. Machine learning (ML) has been identified as a viable approach in this context due to its capacity to allow the development of predictive models that can help in the identification of AD across its various phases. This paper provides an overview of current developments in developing ML models for application in Alzheimer's disease screening and detection. The paper opens with a discussion of the importance of early identification and recognition of Alzheimer's disease by detailing the disease's pathophysiology and describing its clinical signs. Data from neuroimaging (e.g., MRI, PET scans), clinical data (e.g., cognitive evaluations, genetic information), and new biomarkers (e.g., markers in cerebrospinal fluid) are all discussed next. The report then delves into the development of ML methods in AD studies, showing the shift from classical statistical approaches to cutting-edge algorithms like deep learning and ensemble approaches. This article delves into the significance of feature selection and extraction in improving model performance and readability. Moreover, the paper discusses difficulties connected to the accessibility of data, the presence of bias, and the requirement of standardized datasets and evaluation criteria. The review's meat and potatoes are an in-depth look at the latest research and top-tier ML models for AD diagnosis and detection. There is a large variety of techniques that may be used to these models, including as Transfer learning from different domains, semi-supervised learning, supervised learning, and unsupervised learning. The article also discusses how multimodal data fusion techniques could help improve diagnostic precision. The evaluation places equal emphasis on the interpretability and therapeutic usefulness of models as they do on their development. The article talks about how to incorporate model predictions into clinical decision support systems by visualizing and explaining them. Also discussed are the legal and moral complications of implementing ML-based AD diagnostic systems.