Disease Prediction Using Random Forest Machine Learning Algorithm

Abstract

The growing significance of machine learning in healthcare and disease prediction is underscored by its application in the "Disease Prediction" method, which employs predictive modeling to analyze user-input symptoms and forecast potential ailments. Automated disease prediction systems offer a promising solution to the challenges of accessing timely and cost-effective healthcare, particularly for individuals residing far from medical facilities. Leveraging data mining techniques, these systems assess a patient's risk level based on symptoms, facilitating early detection and management of chronic diseases like heart disease and diabetes. Machine learning plays a pivotal role in this domain, empowering predictive models to analyze vast healthcare datasets efficiently. Despite the advancements, there's a need for comprehensive studies integrating machine learning for disease prediction, especially regarding chronic conditions. The proposed framework integrates structured and unstructured data, employing the Random Forest algorithm for accurate predictions. Results demonstrate high accuracy across various diseases, showcasing the potential of machine learning in enhancing healthcare outcomes. However, challenges like dataset biases and overfitting necessitate future research focusing on larger, more representative datasets and advanced modeling techniques. Collaborative efforts between stakeholders can drive the adoption of machine learning-driven predictive models in clinical settings, ushering in a new era of proactive and personalized healthcare.