development of predictive analytics model for disease prediction using machine learning techniques

Abstract

A remarkable amount of research has been proceeding to apply machine learning techniques to produce healthcare solutions due to availability, adaptability and advancement of cloud and web technologies. On the other hand, COVID19 diagnosis process in its current form is facing the problems of shortage of medical resources with high growth of confirmed cases that results in large waiting time for screening of COVID19 patients. Increase in diagnosis time enhances the chances of cross infection. Essential requirement to stop the outbreak is early diagnosis of COVID19 patients. Even though cases are under control, people living to remote places cannot get timely treatment due to unavailability of specialized experts. Machine learning based predictive models can come up with the solution to the issues of COVID19 diagnosis by assisting in initial screening as well as helping experts in decision making. newlineResearchers used machine learning classifiers to predict COVID19 positivity. We also propose a framework to classify a sample with clinically assessed parameters, patient-reported symptoms, past medical histories into COVID19 positive or negative. Novelty of our work includes the method to find optimal subset of features for improved performance of classification. Results of experiments revel that a classifier trained with features selected by the proposed method got better accuracy of around 3% to 12 % compared to a classifier trained on all the features in the dataset. newlineEfforts have been applied to develop COVID19 prediction system using either of chest X-rays, CT scans or clinical parameters. Few works have been done incorporating multi- modal inputs. Most approaches developed to predict disease positivity only without predicting disease severity. Predicting severity of infection is important in resource management and reducing mortality. We propose reliable multimodal framework to classify a sample into mild, moderate or severe class denoting infection severity. Sample includes text data (Patient details, co morbidit