Dharmesh Dhabliya
ABSTRACT
Healthcare research priorities predictive modelling accuracy and effectiveness, especially in cardiovascular disease (CVD) risk assessment and early detection. This transformative study uses advanced data mining techniques to build an unmatched predictive model with 99.37% accuracy. This novel hybrid framework relies on the powerful Random Forest algorithm, which is adaptable and robust. The Random Forest paradigm, which trains many decision trees on a random subset of the data, has been successful in uncovering complex feature interactions and data relationships. The above trait allows it to navigate the complex CVD risk assessment conundrum. Due to its ability to iteratively improve performance, Gradient Boosting is added to the Random Forest framework. Gradient Boosting is used to improve model prediction in this study. This method refines predictive performance by iteratively fixing previous model errors. This study uses an iterative refinement process to capture even the smallest dataset nuances, improving analysis precision. Creating the hybrid model in this study required skill and planning. Random Forest's ability to understand complex feature interactions and Gradient Boosting's iterative precision improve its prediction. The convergence of data mining methods creates a predictive model that outperforms its components, predicting cardiovascular disease risk with 99.37% accuracy. This revolutionary hybrid model has two major implications. This tool helps doctors identify potential cases of CVD and implement personalized care strategies by assessing risk accurately and quickly. This phenomenon also shows the potential that data mining's complex methods unleash when seamlessly integrated. This harmonious integration helps explore uncharted territories and opens new predictive modelling precision frontiers. The convergence of Random Forest and Gradient Boosting methods for cardiovascular disease risk assessment advances predictive modelling. A novel hybrid model with 98.737% accuracy is presented in this study. This model solves complex cardiovascular disease detection and risk mitigation problems, making it a major data mining advancement. By using this innovative approach, data mining techniques can be used to address CVD's formidable challenges. The concept is a symbol that guides the healthcare community towards better diagnostic precision and patient-centered care.
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