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@article{193767,
        author = {Hasthi Teja and M Asha Sree Latha and R Bhavya Sree and K Lokesh and P Bhargav Naidu},
        title = {Alzheimer’s Disease Prediction Using Supervised Machine Learning Models},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {10},
        pages = {1486-1495},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=193767},
        abstract = {Alzheimer’s disease prediction is a vital research area in healthcare due to its impact on memory, cognition, and quality of life. This project presents an efficient Alzheimer’s disease prediction system using supervised machine learning techniques. Clinical and demographic parameters such as age, cholesterol levels, and other health indicators are analysed to determine disease likelihood. Machine learning algorithms, including K-Nearest Neighbour (KNN), Random Forest, Artificial Neural Networks (ANN), and Logistic Regression, are employed for classification. Data preprocessing techniques such as normalisation and feature selection are applied to enhance model performance. The system is tested using metrics such as accuracy, precision, recall, and F1-score. Experimental results show that Logistic Regression achieves the highest accuracy of 98%, followed by K-Nearest Neighbour at 90%, while Random Forest at 89%, and Artificial Neural Networks achieved 87%. The proposed approach supports early detection of Alzheimer’s disease and assists healthcare professionals in informed decision-making.},
        keywords = {Alzheimer’s disease prediction, machine learning, supervised classification, predictive modeling, healthcare analytics, clinical data analysis, early disease detection, decision support systems},
        month = {March},
        }