Data Analytics Measure for Reporting Disease Outbreak in a Region by Prediction of Supervised Machine Learning Approach

Abstract

Generally, healthcare industry has become big business and produces large amounts of health-care data daily that can be used to extract information for predicting disease that can be happen in future. Whereas victimisation the treatment history and health information, this hidden information in the healthcare data will be later used for affective decision making for patient’s health. Also, this area needs improvement by using the informative data in healthcare. To prevent this problem in hospital sectors, must predict whether the disease is happened or not by given attributes from given dataset using machine learning techniques. To propose a machine learning-based method to accurately predict the diseases (heart, diabetes, breast etc.) by prediction results in the form of best accuracy from comparing supervise classification machine learning algorithms. The aim is to investigate machine learning based techniques for patient disease forecasting by prediction results in comparing best accuracy with evaluation of GUI application results. Additionally, to compare and discuss the performance of various machine learning algorithms from the given dataset with evaluation classification report, identify the confusion matrix and to categorizing data from priority and the result shows that the effectiveness of the proposed machine learning algorithm technique can be compared with best accuracy with precision, Recall, F1 Score, sensitivity and specificity.