An Intelligent Data-Driven Framework for Early Prediction and Prevention of Sinusitis Through Advanced Machine Learning Techniques

Sinusitis is a common condition that affects millions of people worldwide, and can cause chronic pain and decreased quality of life. To prevent, early identification of risk factors and timely interventions are crucial. We have a proposed data driven model for prevention of sinusitis based on machine learning techniques for the same. Patient data is gathered and pre-processed demographic, medical history, environmental factors, lifestyle habits etc. to ensure data quality. Several machine learning algorithms including Tree, kNN, Linear Regression and Gradient Boosting are trained to predict the probability of the occurrence of sinusitis. The model runs an analysis of the importance of features to determine the role played by the key risk factors in causing sinusitis. Experimental results have proven that the proposed approach can be successfully used to predict high-risk individuals with high accuracy, which can be used for targeted preventive measures. This research highlights the potential of machine learning in proactive healthcare, offering a scalable and efficient solution for reducing the incidence of sinusitis through personalized prevention strategies.