Early diagnosis of Alzheimer’s Disease is important for the progress of more predominant treatments. Machine learning (ML), a branch of artificial intelligence, provides a variety of probabilistic and upsurge techniques that permits PCs to gain from vast and complex datasets. As a result, researchers concentrate on using machine learning often for diagnosis of early stages of Alzheimer’s Disease. This paper represents a review, analysis and critical evaluation of the recent work done for the early detection of Alzheimer’s Disease using Machine Learning techniques. Several methods achieved promising prediction accuracies, however they were calculated on different pathologically unproven data sets from different imaging modalities making it difficult to compare among them. Moreover, many other factors such as pre-processing, the number of important attributes for feature selection, class imbalance distinctively affect the computation of the prediction accuracy. To overcome these flaws, a model is proposed which comprise of initial pre-processing step followed by imperative attributes selection and classification is achieved using association rule mining. Furthermore, this proposed model-based approach gives the right path for research in early diagnosis of AD and has the potential to distinguish AD from healthy controls.

Alzheimer’s, Mental Disorder, Association Rule Mining

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