Detection of Early Alzheimer’s Risk Using Data-Driven Machine Learning Methods
Detection of Early Alzheimer’s Risk Using Data-Driven Machine Learning Methods
Authors:
Ritesh Kumar Yadav1, Suman Kumar2, Gourav Kumar3 , Ranvir Kumar4
¹ Department of Computer Science/ Adwaita Mission Institute of Technology / Aryabhatta Knowledge University, Patna ²Department of Computer Science / Adwaita Mission Institute of Technology / Aryabhatta Knowledge University, Patna ³Department of Computer Science/ Adwaita Mission Institute of Technology / Aryabhatta Knowledge University, Patna 4Department of Computer Science and Engineering/All Saints' College of Technology/ Rajiv Gandhi Proudyogiki Vishwavidyalaya, Bhopal
Email: riteshkumar607269@gmail.com, Suman06053@gmail.com, dhawaldhonigourav@gmail.com, ranvirdevops@gmail.com
Abstract - Alzheimer’s disease (AD) is a progressive neurodegenerative disorder that primarily affects memory, cognitive functions, and behavioral abilities. Early diagnosis of Alzheimer’s disease is essential because timely intervention can slow disease progression and improve patient care and quality of life. Traditional diagnostic procedures are often unable to detect the disease in its initial stages due to subtle clinical symptoms and limited accessibility to advanced diagnostic tools. This research presents a data-driven machine learning framework for the early prediction of Alzheimer’s disease using cognitive assessment data, medical imaging, and patient health records. The proposed system integrates machine learning algorithms to analyze patient information and predict the likelihood of early-stage Alzheimer’s disease with improved accuracy. The project has been implemented using Python and Flask for backend processing, while HTML, CSS, and JavaScript are used for frontend development. Experimental results indicate that machine learning-based prediction systems can significantly support healthcare professionals in identifying high-risk patients at an earlier stage. The research highlights the importance of artificial intelligence in healthcare and demonstrates the effectiveness of predictive analytics in neurological disease diagnosis [1][2].
Keywords Alzheimer’s Disease, Machine Learning, Early Prediction, Logistic Regression, Linear SVM, Ensemble Learning, Healthcare Analytics, Biomarker Analysis, Flask Framework, Predictive Modeling, Cognitive Impairment, Data-Driven Healthcare.