Disease Diagnosis System using Machine Learning

The efficient use of data mining in virtual sectors such as e-commerce, and commerce has led to its use in other industries. The medical environment is still rich but weaker in technical analysis field. There is a lot of information that can occur within medical systems. Using powerful analytics tools to identify the hidden relationships with the current data trends. Disease is a term that provides a large number of conditions connected to the heath care. These medical conditions describe unexpected health conditions that directly control all the organs of the body. Medical data mining methods such as corporate management mines, classification, integration is used to analyze various types of common physical problems. Separation is an important problem in data mining. Many popular clips make decision trees to produce category models. Data classification is based on the ID3 decision tree algorithm that leads to accuracy, data are estimated to use entropy verification methods based on cross-sectional and segmentation and results are compared. The database used for machine learning is divided into 3 parts - training, testing, and finally validation. This approach uses a training set to train a model and define its appropriate parameters. A test set is required to test a professional model and its standard performance. It is estimated that 70% of people in India can catch common illnesses such as viruses, flu, coughs, colds etc. every two months. Because most people do not realize that common allergies can be symptoms of something very serious, 25% of people suddenly die from ignoring the first normal symptoms. Therefore, identifying or predicting the disease early using machine learning (ML) is very important to avoid any unwanted injuries.