Applying Feature Selection and Ensemble Models to Classify Complex Data Using Lasso Regression and Random Forest in Medical Datasets

The rise of big data has posed major challenges for the healthcare industry on how to analyze intricate medical datasets in order to drive improved patient outcomes and clinical decision making. This paper examines the enhancement of complex medical data classification through ensemble models that are integrated with feature selection techniques such as RFE, LR, and RF. Our objectives are to enhance predictive performance with model interpretability by taking advantage of the strengths of these methods. In this research, we compare RFE, LR and RF on all three medical datasets (Breast Cancer, Pima Diabetes and Chronic Disease Classification) to investigate how effective they are in finding relevant features and how well their ensemble models can run. The findings demonstrate that, RF produces the best performance out of all methods by far, and the performance is even better when it is combined with RFE since RF + RFE performs best on all 6 datasets. Though ensemble models offer enhanced stability, their advantages vary according to the situation, and they may not yield better performance than the best single model in every case. Also, synergism between linear and nonlinear implementations was minimal. Through this study, we demonstrate that applying an appropriate feature selection approach and classification model to the complexity of the medical data is key. These findings provide important insights for improving predictive modeling in health care to inform relevant clinical decision-making and improve patient outcomes. Further studies can inves- tigate more ensemble approaches as well as applying it to a greater data set, which would improve applicability and generalizability