Detection and analysis of Alzheimer's disease using various machine learning algorithms. (2021)

• Record Type: Journal Article
• Authors: Kishore, P.; Usha Kumari, Ch.; Kumar, M.N.V.S.S.; Pavani, T.
• Is Part Of: Materials today. Volume 45:Part 2(2021)
• Page Start: 1502
• Page End: 1508
• Publication Date: 2021
• Subjects: Machine learning -- SVM -- Random forest -- Naïve bayes -- Decision tree; Materials science -- Congresses -- Periodicals; 620.1
• DOI: 10.1016/j.matpr.2020.07.645
• Languages: English
• ISSNs: 2214-7853

Abstract

Highlights: Improved accuracy when compared to existing models. This model can process the MRI images for better analysis and research to produce good outcomes. Sophisticated results which provide useful insights for a particular patient details. Can be extended to a recommendation engine for a better treatment, which replaces the doctors work. Abstract: Alzheimer's is a dynamic ailment that decimates the mind's memory and its general functioning. Unfortunately till now, no single test can diagnosis this disease. Cerebrum checks alone can't be considered as a key factor to decide if the individual is experiencing it or not. As of now, the physician is in a conclusion that an individual is suffering from Alzheimer's on premise of the reports of the relations in regards to the social proclivity and checking the past clinical record. Artificial intelligence along with Machine Learning calculations perhaps in a situation to adjust this model. Big processing, in light of the fact that the data is taken through various sources with complex and creating circumstances that make certain to develop later on. Along these lines, in that, we'll take consequences of what extent level of patients get the illness as positive data and negative data. The proposed arrangement shows a big processing model, from the data mining perspective. Utilizing classifiers, this paper presents the work by preparing Alzheimer's rate and qualities are appearing as a disarray framework using different machine learning algorithms. The earlier research proved that the detection of Alzheimer's disease using Support Vector Machine classifier and obtained very less accuracy. In view of this there is need of increasing the accuracy. So, this paper presenting different algorithms to classify the data to improve the efficiency in detecting the mentioned disease and observed that the Support Vector Machine with linear kernel model gives better accuracy than other models.