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@article{175438,
        author = {Pinto Raj J and Sanjai V and Poolpandi S},
        title = {AI-Based Preventive Healthcare For Economically Constrained Families},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {11},
        number = {11},
        pages = {2934-2941},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=175438},
        abstract = {This article presents a comprehensive framework designed to enhance healthcare accessibility for economically constrained families through advanced artificial intelligence technologies. The proposed Preventive Healthcare System (PHS) integrates supervised machine learning and deep learning models into a Flask-based web application to provide cost-effective, AI-driven disease prediction and medical assistance. The system addresses two primary objectives: delivering early diagnosis of critical illnesses using predictive analytics and offering real-time healthcare support through AI-powered modules. Supervised machine learning algorithms are used to predict common diseases such as diabetes, heart disease, cancer, stroke, and liver disease based on user-input health parameters, while convolutional neural networks (CNNs) detect brain tumors and pneumonia from medical scans. Additionally, the platform features an intelligent chatbot for healthcare queries, a medicine information module, and user authentication with history tracking via SQLite. This innovative solution promotes preventive care and reduces dependency on costly medical consultations, making quality healthcare more accessible to underserved populations.},
        keywords = {Preventive Healthcare System, AI Integration, Disease Prediction, Medical Assistance, Healthcare Accessibility, Economically Disadvantaged, Real-Time Inference.},
        month = {April},
        }