Browsing by Author "Mohamed El-Dosuky"

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    ENHANCING DIABETES CARE VIA ARTIFICIAL INTELLIGENCE
    (Little Lion Scientific, 2024-09-30) Turja Bhattacharjee; Mohamed El-Dosuky; Sherif Kamel
    Artificial intelligence (AI) has become a potent tool in healthcare with the potential to completely change the way diabetes is treated. This study investigates how AI affects patient outcomes and diabetes treatment. Healthcare providers can extract insightful information from patient data using machine learning, data analytics, and AI-driven wearable devices, resulting in individualized treatment programs and better glycemic control. AI chatbots and virtual assistants improve patient support and engagement, encouraging improved treatment adherence. Despite privacy and ethical issues, AI is effective at cutting healthcare expenses and improving the quality of life for patients is obvious. Healthcare providers can use AI to develop a patient-centered strategy and improve diabetes care by working with researchers and politicians. This paper proposes a smart chatbot for enhancing diabetes care through natural language interactions. The chatbot's architecture uses pattern matching and keyword identification techniques to follow a multi-level interaction procedure. The proposed chatbot system simplifies diabetes diagnosis by using natural language interactions, asking questions based on previous responses through a multi-level diagnostic flow. It employs AIML-based memory techniques and pattern matching to identify keywords at each level, ensuring relevance and coherence in conversation. The system follows a search engine-like flow, using methods like the Sequence Words Deleted (SWD) technique and Triangular Number equation to optimize keyword matching, with Vpath values guiding the diagnostic path. The chatbot enhances patient diagnosis by providing structured, personalized guidance through these techniques.