Scopul acestei lucrări este de a dezvolta modele predictive bazate pe inteligența artificială pentru identificarea riscului de diabet, sprijinind astfel diagnosticul timpuriu si optimizarea resurselor medicale. Lucrarea explorează integrarea solutiilor AI pentru a sprijini personalul medical și a îmbunătăți rezultatele clinice.
The objective of this work is to develop AI-based predictive models for identifying dia-betes risk, thereby supporting early diagnosis and optimizing medical resources. The thesis explores the integration of AI solutions to assist medical professionals and improve clinical outcomes.Tools Used: Python, Google Colab, machine learning libraries (Scikit-learn, XGBoost, LightGBM). Structure: The structure of the thesis includes a list of figures, a list of code listings, an introduction, five chapters, a conclusion, and a bibliography. Chapters: Chapter 1: Ethical and Social Implications of AI in Diabetes Prediction; Chapter 2: Challenges and Limitations in AI-based Diabetes Prediction; Chapter 3: Strategies for AI Implementation in Diabetes Healthcare; Chapter 4: Evaluating AI Models for Diabetes Prediction; Chapter 5: Future Directions for AI in Diabetes Prediction and Management.
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