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@article{152542,
        author = {D. Lyakath and Dr.S. Tamil Selvan},
        title = {Optimized Machine Learning Classification Techniques for Cardio Disease Identification},
        journal = {International Journal of Innovative Research in Technology},
        year = {},
        volume = {8},
        number = {3},
        pages = {819-824},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=152542},
        abstract = {Cardio problems are one of the major reasons of death in the world. Cardio illness is major complicated diseases and worldwide many people faced from this disease. On time and effective identified of cardio disease plays an important role in healthcare, mainly in the arena of cardiology. In this research, we propose an optimized Technique that targets at identifying the most important policies by applying machine learning methods subsequent in increasing the correctness in the forecast of cardio hypotension. we projected an effective and exact classification to diagnosis cardio disease and the scheme is created on machine learning methods. We also projected innovative fast provisional common data feature collection procedure to resolve feature selection problem. The structures selection procedures are used for structures selection to growth the arrangement correctness and decrease the implementation time of arrangement scheme. Moreover, the authority one subject obtainable cross-validation technique has been used for knowledge the best performs of classical calculation and for hyperparameter modification. We produce an improved performance level with an accurateness level of 90.2% accomplished the forecast model for cardio sickness with the optimized random Prediction with a linear model Moreover, the projected scheme can simply be executed in healthcare for the recognition of cardio disease. },
        keywords = {Cardio disease classification, features selection, illness analysis, intellectual scheme, health information analytics.},
        month = {},
        }