Optimizing Predictive Healthcare Modelling in a Cloud Computing Environment Using Histogram-Based Gradient Boosting, MARS, and SoftMax Regression

Abstract

Background information: Predictive modeling is increasingly vital in the healthcare sector for early disease identification and personalized treatment. Large, intricate datasets pose challenges for conventional methods; cloud computing can assist by providing scalable, efficient processing. This research examines the combination of MARS, SoftMax Regression, and Histogram-Based Gradient Boosting within a cloud-based framework, demonstrating improved precision in predicting health outcomes. The proposed method enhances decision- making and treatment efficacy by utilizing robust prediction models. Methods: Advanced machine learning methods, such as MARS, SoftMax Regression, and Histogram-Based Gradient Boosting, are used in this cloud-based study. These algorithms are used to optimise predictive modelling after pre-processing data from healthcare sources. Model efficacy is revealed by evaluating performance using criteria such as accuracy, precision, recall, and F1-score. Objectives: The main objective is to improve the accuracy of healthcare predictions through the use of advanced algorithms—Histogram-Based Gradient Boosting, MARS, and SoftMax
Regression—in a cloud computing environment. Moreover, the initiative aims to evaluate how cloud infrastructure enhances the scalability and computational efficiency of predictive models, which could ultimately result in better patient outcomes and decision-making in real healthcare environments. Results: The suggested model demonstrated excellent predictive accuracy, with indicators showing considerable enhancements compared to conventional techniques. In particular, the integrated method produced better precision, recall, and F1-score metrics, underscoring the efficacy of cloud-based computing. Findings indicated a significant enhancement in classification precision and dependability, endorsing its application in intricate healthcare predictive analytics.
Conclusions: The incorporation of sophisticated machine learning techniques within a cloud computing environment presents considerable opportunities for improving predictive healthcare modeling. This method overcomes significant constraints of conventional techniques, providing precise and effective forecasts. These models may enhance patient outcomes by enabling prompt and knowledgeable decision-making in healthcare settings.