Case Study on Early-Stage Risk Prediction by Machine Learning Algorithms

Abstract

In the healthcare sector, we generally require continuous monitoring of the patient, The model is designed to assist patients in understanding the potential hazards associated with the infectious illness by using Machine Learning algorithms. It provides recommendations as per the infection severity, enabling patients to effectively monitor their condition independently. All the data records are initialized in the dataset, these are stored in the database which helps in more accuracy of illness prediction. This software interface is simple, based on symptoms of the patient the algorithms will process and gives appropriate infection details, suggestions on severity of infection, recommends to consult a doctor or not. It also ensures the seasonal diseases ongoing in particular areas, identifies them, and gives regular alerts to the users. Non-Communicable Diseases (NCDs) are currently causing more infections are highlighted, and alerts the user by giving information and precautions to be taken for improvement of health. Predictive models and classification algorithms, examine the symptoms specified by the patient as input. Then the most possible disease name will be displayed as an output. Decision Tree, Naive Bayes Classifier, and Random Forest Algorithm are used to forecast the disease. Disease prediction is accomplished by employing ML Algorithms.