Disease Prediction Using Artificial Intelligence
Keywords:
Artificial Intelligence, Disease Prediction, Machine Learning, Medical Diagnostics, Healthcare Technology, Early Detection, Data Analysis, Personalized MedicineAbstract
Artificial intelligence (AI) is increasingly recognized as a transformative tool in healthcare, offering advanced capabilities in disease prediction through data-driven modeling. The proliferation of big data in medical diagnostics has rendered traditional analytical approaches insufficient, particularly for identifying complex patterns in patient health. Most AI models are built on Western datasets and lack contextual adaptability for developing countries, including Uzbekistan, where unique environmental and genetic factors affect disease manifestation. This study aims to develop and evaluate a hybrid AI-based disease prediction system that integrates regional environmental, lifestyle, and genetic data, tailored to Uzbekistan’s healthcare context. The proposed model enhances diagnostic accuracy and supports early detection of chronic diseases such as cardiovascular disorders and diabetes. It demonstrates the feasibility of cloud-based implementation across both urban and rural clinics, ensuring broader accessibility. The approach uniquely incorporates localized factors such as air quality and hereditary patterns into AI algorithms, enabling culturally relevant and personalized health assessments. Moreover, it employs explainable AI and federated learning to improve transparency and data privacy. This research has practical value for national health policy and infrastructure, offering a scalable, ethical, and predictive tool for improving public health outcomes. It may serve as a prototype for similar health systems in developing countries facing comparable demographic and resource challenges
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