Online Medical App Using ML

Abstract

Disease Prediction Using Machine Learning is the system that is used to predict diseases based on the symptoms that are given by patients or any other user. Disease prediction in humans also means predicting the probability of a patient’s disease after examining the combinations of the patient’s symptoms. This analysis in the medical industry would lead to a streamlined and expedited treatment of patients. The previous researchers have primarily emphasized machine learning models, mainly Support Vector Machine (SVM), K nearest neighbors (KNN), for the detection of diseases with symptoms as parameters. However, the data used by the prior researchers for training the model is not transformed, and the model is completely dependent on the symptoms, while their accuracy is poor. Nevertheless, there is a need to design a modified model for better accuracy and early prediction of human disease. The proposed model has improved the efficacy and accuracy of the model by resolving the issue of the earlier researcher’s models. The proposed model uses the medical dataset from Kaggle and transforms the data by assigning weights based on their rarity. This dataset is then trained using a combination of machine learning algorithms, including SVM. Neural Network Parallel to this, the history of the patient can be analyzed using the LSTM Algorithm. SVM is then used to conclude the possible disease. The proposed model has achieved better accuracy and reliability as compared to state of the art methods. The proposed model is useful to contribute towards development in the automation of the healthcare industries. It will be connected to Hospital websites, and only an authenticated user (a doctor) will be connected to the main predicting Web Application for a possible prediction.