Early Detection of Alzheimer's Disease Using Machine Learning Techniques

Alzheimer's disease (AD) is a disease of the central nervous system that manifests as dementia and other mental disorders. It's critical to correctly diagnose Alzheimer's disease as well as in organising the treatment of the illness and early intervention. Current inventive advancements in Machine learning (ML) has the potential to improve diagnosis in the early stages of AD. In this work, we make use of clinical, genetic, and neuroimaging data to contrast several machine learning algorithms, such as CNNs, gradient boosted classifiers, and SVMs. We also review some of the issues arising when applying these models such as previous knowledge, choice of features, and quality of the models. Our findings also reveal that CNNs are effective in learning features from neuroimaging datasets, wherein SVM and gradient boosting classifiers also give a significant account on the clinical and the genetic data. New possibilities in the development of machine learning (ML) are appearing and can assist in increasing the detection rate of Alzheimer's disease with the help of utilizing large neuroimaging, genetics, and medical data. Traditional methods of analysis often cannot identify these signals but through machine learning techniques, the signals can easily be identified from this data. Last of all, we deliberate on the drawbacks of the current approach to machine learning and the potential lines of investigation.