Machine Learning Models for Predicting Post-Treatment Complications Using Cloud-Processed Data

Abstract

Predicting post-treatment problems is crucial for improving patient outcomes and reducing pressures on the healthcare system. This paper presents a machine learning strategy using Linear Regression (LR) to forecast post-treatment problems utilizing cloud-processed healthcare data. LR models ascertain relationships among treatment factors, patient demographics, and clinical outcomes, yielding useful information for physicians. Cloud-based architecture facilitates the storing, processing, and real-time analysis of extensive medical information, providing safe and effective data management. Utilizing cloud computing, the model may expand across various healthcare environments, providing a flexible solution for distinct patient demographics. The interpretability and clarity of LR represents it especially appropriate for healthcare applications, where comprehending the correlations between input variables and predicted results is essential. The findings indicate that this methodology may enhance clinical decision-making, facilitating personalized treatment and proactive problem control. It advances the expanding domain of predictive analytics in healthcare, highlighting the need for scalable, elucidative, and efficient solutions.

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