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@article{169186,
        author = {PULIPATI MADHURYA and D MURALI},
        title = {AN INTEGRATED MACHINE LEARNING FRAMEWORK FOR EFFECTIVE PREDICTION OF CARDIOVASCULAR DISEASES},
        journal = {International Journal of Innovative Research in Technology},
        year = {2024},
        volume = {11},
        number = {6},
        pages = {751-755},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=169186},
        abstract = {Cardiovascular disorders are regarded as the most dangerous conditions, having the greatest fatality rate worldwide. They have become exceedingly common over time and are now overstretching national healthcare systems. High blood pressure, family history, stress, age, gender, cholesterol, BMI, and an unhealthy lifestyle are all key risk factors for cardiovascular disease. Researchers have proposed numerous ways for early diagnosis based on these criteria. However, due to the intrinsic criticality and life-threatening hazards of cardiovascular disorders, the accuracy of offered procedures and approaches need specific modifications. A Malc add framework is proposed in this study for the effective and precise prediction of cardiovascular disorders. The methodology, in particular, addresses missing values and data imbalances first.},
        keywords = {Cardiovascular disorders, Fatality rate, Risk factors, Early diagnosis, Healthcare systems, MaLCaDD framework, Prediction accuracy, Feature importance, Machine learning ensemble, Logistic Regression},
        month = {November},
        }