Disease Classification and Prediction using Ensemble MachineLearning Classification Algorithm

Abstract: In today’s scenario, disease prediction plays an important role in medical field. Early detection of diseases is essential because of the fast food habits and life. In my previous study for predicting diseases using radiology test report , and to classify the disease as positive or negative three classifiers Naïve Bayes (NB), Support Vector Machine (SVM) and Modified Extreme Learning Machine (MELM was used to increase the accuracy of results. To increase the efficiency of predicting the disease and to find which disease pricks the society, ensemble machine learning algorithm is used. The huge data from the healthcare industry were preprocessed., categorized and analyzed to find out and predict which patient to be treated and given priority and which hits the society the most. Ensemble machine learning's popularity in the medical industry is due to a variety of factors the Classifiers used are K Nearest Neighbors, Nearest Mean Classifier, Mean Feature Voting Classifier, KDtree KNN, Random Forest. To reduce the manual processes in medical field automating these processes has become important. Electronic medical records and significant advances in health care have given an opportunity to make find out which patients need to be given more importance. Several methodologies and techniques were used to preprocess the data in order to meet the study' requirements. To improve the performance of machine learning algorithms, feature selections were made using Tabu search. When ensemble prediction is combined with the Random Forest algorithm as the combiner, the results are more reliable. The aim of this study is to create a system to classify Medical records whether it is diseased or not and find out which disease rate has increased. This research will help the society to an individual to get treated easily and take preventive measures to avoid diseases.Published By: Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP) © Copyright: All rights reserved