Early Prediction of Alzheimer’s Disease with the Help of Machine Learning Approach

Abstract

Introduction: Alzheimer’s Disease is kind of neurodegenerative disorder under neurological disorder.Neurological disorder is disease of the central nervous system.Nervers,brain and spinal cord all working together and control whole the  working of human body.Alzheimer’s disease is brain related disease. if it is not cured within time limit. it getting crucial to preventing and controlling its progression.

Objectives: various approaches  for diagnosis of alzheimer’s disease and also examine  various algorithm of  machine learning approaches for prediction of alzheimer’s disease.Classification and daignosis that have been applied on different datasets.

Methods: two different kind of dataset for the research.one is image dataset is used for the research and text dataset used as well.machine learning algorithms like 3D CNN and ANN applied on both the dataset.we try to prove that for the prediction of alzheimer’s disease with the help of machine learning approaches gives better and faster result.so the patient’s medication start immediately without any dealy.it is beneficial for the patient and doctor as well so patient to get medication in early stage and  recover fastly.

Results: We demonstrated the effectiveness of our method empirically in terms of sensitivity, specificity, and accuracy. Although the obtained results (more than 90% accuracy) are superior to those of the majority of prior research, more development and extensive enhancements are required to improve the model for the diagnosis of AD. Similar to a diagnostic tool, the suggested modeling technique can find its appropriate use. Additionally, when assessing AD treatment procedures

Conclusions: The ML classifier employed in this study has no bearing on the extraction and selection of text data or MRI features.Consequently, the doctors are given a combination of several characteristics that are suggestive of the identification and thorough observation of the condition. Our strategy chose the neural network instead of the traditional way. Consequently, the utmost precision is achieved.