• Shaurya Magar
• Shazia Talib
• Nihal Vishal Gupta
• Anushka Nandkumar Mulik
• Prachi Tawde

Recently, neurodegenerative diseases have become a significant emerging global health issue. There is a huge unmet need for effective treatments for neurodegenerative diseases such as Alzheimer’s disease (AD), dementia, Parkinson's disease, and more. Alzheimer’s disease is a brain disorder that primarily affects older adults. It slowly destroys thinking ability and memory and is the most common cause of dementia. Screening for Alzheimer’s disease has proven to be complicated and expensive. There is no simple model to examine Alzheimer’s. Furthermore, it generally requires highly specialized personnel and clinical settings. In recent years, an increase in interest in Machine learning has been observed in the field of medicine. Machine learning is a subset of Artificial Intelligence facilitating scientists, clinicians, doctors, etc. with the means to predict neurodegenerative diseases, enabling them to address challenges like early diagnosis and effective treatment of these diseases. Numerous techniques and algorithms to resolve this problem exist. Choosing the appropriate algorithm is crucial for obtaining reliable and accurate results. The techniques which can identify the best parameters for Alzheimer’s disease prediction are Regression, Decision Tree, Support Vector Machine, Random Forest, etc. The aim of this paper is to compare and analyze the proficiency of Logistic Regression, Decision Tree, and Support Vector Machine in the prediction of Alzheimer’s to conclude which technique produces the most effective outcomes. It addresses the reliability of each machine learning algorithm to detect the disease and initiate early treatment prematurely.

Alzheimer’s Disease, Machine Learning, Support Vector Kernel, Logistic Regression, Decision Tree

"Comparing Performances of various Machine Learning Algorithms in forecasting Alzheimer’s disease", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.9, Issue 12, page no.b794-b801, December-2022, Available :http://www.jetir.org/papers/JETIR2212188.pdf

"Comparing Performances of various Machine Learning Algorithms in forecasting Alzheimer’s disease", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.9, Issue 12, page no. ppb794-b801, December-2022, Available at : http://www.jetir.org/papers/JETIR2212188.pdf