A Machine Learning based Model for Disease Prediction

People are now suffering from a variety of diseases as a result of the environment in which they live and their lifestyle choices. As a result, the goal of predicting disease at an earlier stage becomes increasingly critical. However, making an accurate prediction based on symptoms becomes too tough for doctors to do. The task of accurately predicting disease is one of the most difficult. Data mining is critical in overcoming this difficulty because it may be used to forecast the sickness. Every year, a great amount of data is generated in the field of medicine. Due to the extreme increase in the rate of information being collected in the health and medical industries, it has been possible to conduct precise analyses of medical data, which now has resulted in better patient outcomes. When disease data is used as a starting point, data mining can be used to identify hidden patterns in the huge number of medical data that currently exists. On the basis of the patient's symptoms, we suggested a generic disease prediction model. In ability to implement credible illness predictions, we apply machine learning methods such as convolutional neural networks (CNNs) for disease prediction. Disease symptom datasets are essential for disease forecasting purposes. In this general disease prediction model, the individual's lifestyle behaviour as well as examination data are taken into consideration for reliable disease prediction. It has been demonstrated that the accuracy of generalized predictive modeling that used the CNN algorithm is 98.7 percent, which really is better than those of the present technique. In addition, the time and memory requirements for existing mechanism are higher than those for CNN. When general disease is expected, this method is qualified to determine the threat related to institutional disease, which can be stronger or weaker than the previously mentioned of general disease.