Publication

Abstract : Alzheimer’s disease involves a gradual decline in mental function, characterized by the accumulation of abnormal protein deposits around brain cells, resulting in irreversible neural cells death. Unfortunately, existing medications and procedures cannot regenerate these neurons. As the disease progresses symptoms of forgetfulness worsens, significantly impacting daily routines and lifestyle. The progression of Alzheimer’s disease is typically divided into three phases. The preclinical phase occurs when there are no noticeable symptoms, but early Alzheimer’s biomarkers are present. The prodromal phase, or mild cognitive impairment (MCI), is characterized by mild cognitive decline that does not significantly affect daily activities. Lastly, dementia represents the final phase, where moderate to severe cognitive impairment prevents independent living. Early prediction of Alzheimer’s onset is crucial to mitigate its progression. Machine learning (ML) and artificial intelligence (AI) models play a pivotal role in predicting the disease based on health and lifestyle parameters. These advanced technologies enable early detection, offering hope for interventions to delay or prevent further cognitive decline. The Machine learning model is built by developing 4 different models to get better base for disease prediction and after that the results are described using ExplainableAI tools such as SHAP and LIME as the healthcare domain needs explanation for all the results. Using this model we are getting an accuracy of around 94%-100%.