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@article{194822,
        author = {Dr.B Arun Kumar and A. Siri Chandana and G. Sai Saranya and G. Sai Divya and A. Venkata Sirisha},
        title = {PUBLIC HEALTH DATA ANALYSIS FOR DISEASE PATTERN IDENTIFICATION},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {10},
        pages = {8055-8060},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=194822},
        abstract = {Public health data gave by hospitals, clinics, and healthcare monitoring systems continues to increase, posing both possibilities and obstacles for accurate analysis. While such data may offer useful information on disease prevalence and population health trends, human analysis approaches are frequently inefficient, fragmented, and unable to uncover subtle linkages between numerous health markers. This can impede early detection of illness trends and inhibit informed decision-making in public health management. This work investigates a machine learning-based strategy for analyzing public health data and identifying important illness trends. To increase the integrity of the data and statistical readiness, the dataset received is preprocessed with processes such as data cleansing, incorrect value handling, and categorized attribute encoding. The Random Forest classification algorithm is then used to investigate correlations between characteristics and offer predictive insights into illness incidence. The findings show that the suggested method may efficiently convert raw healthcare data into interpretable patterns and trend information. The system has the potential to help healthcare professionals, researchers, and policymakers understand public health dynamics and design timely preventive strategies for better overall health result via providing quicker analysis and more effective visualization disease circulation.},
        keywords = {Public Health Data, Disease Pattern Identification, Machine Learning, Random Forest, Data Analysis.},
        month = {March},
        }