International Research Journal of Engineering and Technology (IRJET)
e-ISSN: 2395-0056
Volume: 12 Issue: 02 | Feb 2025
p-ISSN: 2395-0072
www.irjet.net
ALZHEIMER’S PREDICTION USING MACHINE LEARNING ANUPAMA BENNY1, ELIZABETH SEBASTIAN2, HEPHZIBAH A PERSIS3, RAGENDU R4, ATHIRA R KURUP5, DR.B.BEN SUJITHA6 1-4 BTECH UG Students, Dept of Computer Science and Engineering, TOMS College of Engineering 5Assistant Professor, Dept of Computer Science and Engineering, TOMS College of Engineering
3Professor, Dept of Computer Science and Engineering, Noorul Islam Centre for Higher Education
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Abstract - Alzheimer’s disease AD is a progressive neurodegenerative disorder that involves cognitive decline and memory loss. Early detection is, therefore essential for effective intervention and improved patient outcomes. This paper explores the application of machine learning in AD detection, focusing on advancements in feature extraction, model development, multi-modal data integration, and explainable AI. Through diverse sources of data including imaging, genetic, clinical, and cognitive data, ML methods have shown potential for improving the diagnostic accuracy and detection of crucial biomarkers in the disease. The advancements in deep learning, specifically in convolutional and recurrent neural networks, and in more advanced data fusion techniques have also enabled much broader analyses of changes related to AD. Nevertheless, significant challenges include limited data, poor model interpretability, ethical issues, and practical barriers to implementation in clinical practice. Standardized datasets and federated learning have to be adopted to address such issues. In the paper, future opportunities were highlighted: wearables and real-time monitoring are some of them, emphasizing that ML is transformational in furthering the advance of AD detection and management Key Words: Artificial Intelligence, Machine Learning, Predictive Analytics, Health Data, Disease Prediction, Personalized Medicine, Data Security, Healthcare Innovation.
1.INTRODUCTION Alzheimer’s disease is a complex neurodegenerative disorder and the leading cause of dementia worldwide, significantly affecting patients’ cognitive abilities, including memory, reasoning, and decision-making. The gradual and often subtle onset of symptoms presents substantial challenges for timely diagnosis and intervention. Traditional diagnostic methods, reliant on clinical assessments and neuroimaging, often face limitations due to their subjectivity, high costs, and time consumption. This project pursues overcoming some of these barriers by looking into applications of ML techniques for the earlier detection and prognosis of Alzheimer’s disease. Utilizing high-volume, mixed datasets consisting of voluminous imaging data combined with genetic details, cognitive studies, and other clinical reports, the proposed system aims to understand the subtle features of Alzheimer’s progression. The primary objective is to develop an accurate and scalable ML-based prediction system that not only identifies at-risk individuals but also tracks the progression of the disease to help in effective treatment planning. This project aims to provide a non-invasive, cost-effective, and interpretable diagnostic tool through the integration of multimodal data and advanced algorithms, which can significantly revolutionize the management and care of Alzheimer’s patients.
2. OBJECTIVES The aim of this review is to determine the role of instrumental learning (ML) in overcoming the limitations of traditional approaches to the diagnosis of Alzheimer’s disease. Combines multiple computational elements, including neuroimaging, genetics, medical, and psychological data, to refine the accuracy and precision of diagnosis. Address the complexities associated with translation and clinical acceptance of ML models. Offer scalable and accessible solutions for early diagnosis and control of Alzheimer’s disease.
3. METHODS Study Design: The study was designed to develop and evaluate a machine learning (ML)-based system for the early detection and progression prediction of Alzheimer’s disease (AD). The methodology included data collection, preprocessing, model development, and evaluation. Diverse datasets were utilized, including imaging data, genetic profiles, cognitive assessments, and clinical records. The experimental groups included individuals diagnosed with Alzheimer’s disease (AD), those with mild cognitive impairment (MCI), and healthy controls (CON).
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