ABSTRACT
Due to their circumstances and way of life, people today deal with a wide variety of ailments. Therefore, it will be crucial to predict contamination early. However, based just on symptoms, doctors often fail to make accurate forecasts. The hardest challenge is predicting diseases effectively. Based mostly on the patient's symptoms, it created a complete contamination forecast. It is using the equipment to familiarize ourselves with the Convolutional Neural Network (CNN) and the Adaptive Community-based Fuzzy Inference System (ANFIS) techniques for reliable disease prediction. For an accurate projection, this current illness prediction considers the character's dietary preferences and activity history. ANFIS outperforms CNN's guidelines for popular infection prediction, with a rate of 96.7% accuracy. Additionally, CNN consumes extra memory and processing energy than ANFIS because it trains and assesses facts from the UCI repository.
