Predictive Healthcare Analytics: A Comparative Study of various Machine Learning Algorithms for Disease Prediction
Keywords:
Data mining, Decision Tree (DT), Gradient Boosting (GB), Healthcare, Machine Learning (ML), Naïve Bayes (NB), Prediction systems, Random Forest (RF)Abstract
The Smart Health Disease Prediction project is designed to leverage data mining and machine learning techniques to forecast the development of medical conditions based on patient details and symptoms. Its primary objective is to support healthcare workers in making well-informed decisions and delivering timely medications. This is facilitated by utilizing a virtual intelligent healthcare system that guides users throughout the disease prediction process. A vital component of this project is the Naive Bayes model, which uses training data to estimate the likelihood of various medical conditions given specific symptoms. By incorporating machine learning algorithms such as Decision Tree (DT), Random Forest (RF), Gradient Boosting (GB), and Naive Bayes (NB), the proposed model optimizes features and enhances prediction accuracy. The ultimate aim of the Smart Health Disease Prediction project is to empower healthcare professionals to detect diseases at an early stage enabling prompt interventions and treatments. This proactive approach can significantly improve patient outcomes and overall healthcare efficiency.
