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[1].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.
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DOI:
10.17148/IJARCCE.2022.11418
