Data Mining-Driven Multi-Feature Selection for Chronic Disease Forecasting
DOI:
https://doi.org/10.52783/jns.v14.1993Keywords:
Synergistic Feature Engineering, chronic disease, early prediction, machine learning, Voting Classifier, Ensemble learningAbstract
a major worldwide health difficulty, continual sicknesses call for higher predictive fashions for early diagnosis and individualized treatment. the usage of a synergistic blend of strategies, this technique combines Recursive characteristic removal with pass-Validation (RFECV) and support Vector machine (SVM) for best characteristic selection throughout 8 wonderful datasets: Breast cancer, chronic Kidney, Diabetes danger, Erbil heart sickness, coronary heart disorder, Kidney disease, Pima Indians, and Wisconsin Breast. The technique stresses efficient dimensionality reduction so that the most pertinent facts is applied to improve model overall performance. With a voting Classifier combining AdaBoost decision Tree and ExtraTree obtaining outstanding performance across all datasets, ensemble learning is absolutely important. High-performance results from this era show its dependability and relevance for early continual disorder prediction. The method provides brilliant possibility for enhancing diagnostic accuracy and allowing spark off remedies by tackling feature selection issues and imposing ensemble learning, thereby enhancing healthcare management and results.
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