• Nishant Jha
• Kishan Kumar Sharma
• Ankit Pandit
• Neha Sharma

Early diagnosis is a major aspect for treating any disease and Machine Learning (ML) plays a vital role in speeding up the diagnosis process and increase the efficiency of existing treatment strategies. A short yet effective review of the current trends in diagnosing Alzheimer’s with the help of Machine Learning has been done in this paper. This This review will help in our future work or other contributors working on Drug Repurposing Techniques. In this review, we have found that the Models used in various studies such as ANN, SVM, DNN, CNN etc have achieved accuracy greater than 90% which is a good sign for future work. Upon analysis we have found that various studies have used MRI images for classifying Alzheimer’s. With advancements in computer aided systems, production of quality information from the image processing techniques has introduced various challenges and these challenges are tried to resolve through ML. As the number of studies focusing on the use of ML has increased, the efficiency of the diagnostic systems has also increased and are helping the medical community.

Machine Learning; Alzheimer’s; Image Processing; Drug Repurposing; Computer aided systems

"Machine Learning For Detection and Classification of Alzheimer’s: An Analysis of Current Trends and Future Possibilities", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.8, Issue 4, page no.102-108, April-2021, Available :http://www.jetir.org/papers/JETIR2104115.pdf

"Machine Learning For Detection and Classification of Alzheimer’s: An Analysis of Current Trends and Future Possibilities", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.8, Issue 4, page no. pp102-108, April-2021, Available at : http://www.jetir.org/papers/JETIR2104115.pdf