A Machine Learning Model for Improved Prediction of Alzheimer's Progression

  • Kirubasri G, Kiruthika S

Abstract

Alzheimer’s disease is a severe disorder in the nervous system due to the deposits of proteins like amino and tau causes demise in brain cells. It leads to many adverse effects in human body includes loss of memory, cerebral weakness. Foresee the Alzheimer’s disease has been researched widely in worldwide health organisations.It is a prolonged, irreparable sickness that causes loss of cerebral functioning. It slowly reduces and finally ends the reasoning ability, memory power, visualizing and grasping control. Alzheimer’s Association found that aging is the key threat and commonly the people of age 65 and above are highly affected by this disease. It is an intensifying disease that gets worse over the period and has no complete cure but temporary treatment can helps to lead the life if it is identified at the earlier stage. Estimating the depth of Alzheimer’s in its premature stage is a difficult task since it involved with lots of parameters. The objective of the proposed design is to bring a machine learning model that predicts Alzheimer’s disease precisely in its early stage by using classification algorithm called logistic regression and the output is displayed using web application. The proposed system can help the doctors or medical technicians to diagnose Alzheimer’s disease more accurately.

Keywords: Logistic regression (LR), Web Application, Flask connectivity, Classification Algorithm (CA), Mini Mental State Examination value (MMSE), Mild Cognitive Impairment (MCI).

Published
2020-05-19
How to Cite
Kirubasri G, Kiruthika S. (2020). A Machine Learning Model for Improved Prediction of Alzheimer’s Progression. International Journal of Advanced Science and Technology, 29(06), 4204 - 4215. Retrieved from https://sersc.org/journals/index.php/IJAST/article/view/16426