Intelligent Multiple Diseases Prediction System Using Machine Learning Algorithm

Abstract

AbstractAs a result of their surroundings and lifestyle choices, people nowadays suffer from a wide range of ailments. As a result, predicting illness at an early stage is crucial. Doctors, on the other hand, struggle to make accurate diagnoses based solely on symptoms. The most challenging task is predicting sickness properly. Machine learning plays a key part in forecasting in order to complete this difficult task. To tackle this challenge, machine learning plays a key role in illness prediction. Medical research creates a vast amount of data every year. Early patient care has benefitted from effective medical data analysis because of the rising quantity of data growth in the medical and healthcare professions. In data mining, disease data is utilised to identify hidden patterns in huge volumes of medical data. Based on the patient's symptoms, we created a broad disease prediction. Machine learning algorithms like ANFIS and CNN are used to properly predict sickness (adaptive network-based fuzzy inference system). The collection of illness symptoms is necessary for disease prediction. For an accurate prognosis, this general illness prediction takes into account the person's lifestyle and medical history. When it comes to illness prediction, ANFIS outperforms CNN by a wide margin (96.7%). ANFIS, on the other hand, does not require as much time or memory to train and test because it does not use the UCI repository dataset. There are several libraries and header files included with the Anaconda (Jupyter) notebook that make Python programming more precise and accurate.KeywordsCNNANFISMachine learningDisease prediction