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With the exponential growth of healthcare data, disease prediction has become a crucial area of research in medical data analytics. This paper presents an approach that combines data mining techniques and machine learning algorithms to predict diseases accurately and efficiently. The proposed system analyzes patient data such as medical history, symptoms, lifestyle patterns, and clinical test results to identify potential health risks. Data preprocessing and feature selection are performed to eliminate noise and improve prediction accuracy. Machine learning models such as Decision Trees, Random Forest, Naïve Bayes, and Support Vector Machines (SVM) are applied to classify and predict diseases based on extracted features. Comparative analysis demonstrates that hybrid and ensemble models outperform traditional statistical methods in terms of precision and recall. The integration of data mining with machine learning enables the discovery of hidden patterns and correlations within healthcare datasets, supporting early diagnosis and preventive healthcare strategies. This research contributes to the development of intelligent, data-driven medical decision-support systems.
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Conflict of interest: The author declares that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
Publisher’s note: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
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