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@article{197895,
        author = {Rayan Firdous and Mahima Sharma and Simran Bharti},
        title = {Machine Learning Based Early Prediction Of Genetic Diseases},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {11},
        pages = {7901-7909},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=197895},
        abstract = {Genetic diseases are caused by changes in DNA and can be difficult to detect early because symptoms often appear late. This delay can make treatment less effective and increase health risks. Being able to predict such diseases in advance can help doctors take early action and provide better, more personalized care. In this study, a machine learning approach is used to analyze both genetic and clinical data for early prediction. The process involves cleaning the data, selecting the most important features, and applying models like Random Forest, SVM, and Neural Networks. Techniques like PCA help simplify complex data. The results show that Random Forest performs especially well in predicting diseases accurately.},
        keywords = {Classification Algorithms, Genetic Disease Prediction, Genomic Data, Healthcare Analytics, Machine Learning.},
        month = {April},
        }