Abstract: The current medical system focuses on specific, well-known diseases and is unable to accurately diagnose and predict disease based on early symptoms. These models use a variety of patient characteristics to balance the probability of an outcome over some time and to harness the power to improve decision-making and personal care. Discovering hidden patterns and collaborations from a medical website and the growing testing of a predictable disease model is essential. This paper aims to design a model which can easily diagnose various diseases relying on their symptoms. The model evaluates the user’s symptoms as input and returns the disease probability as an output.The disease probability is calculated by making use of the naive bayes classifier. Therefore this research paper will attempt to apply machine learning activities to health facilities in a particular program. The proposed web-based forecasting app uses the Naive Bayes Algorithm and Decision Tree, a machine learning method as a diagnostic separator based on real-life clinical information.
Keywords: Machine learning, Naive Bayes, Decision Tree, Disease Prediction.
| DOI: 10.17148/IJARCCE.2022.11418