Improving Healthcare with Machine Learning and Deep Learning
Using disease data, MRIs, chest X-rays, and diabetes records, machine learning (ML) assists in diagnosing and treating various conditions and predicts illnesses using techniques such as SVM, Decision Trees, Random Forest Classifier, Gaussian Bayes and Multinomial Bayes, and Gradient Boost.
Abstract
Due to the evolving lifestyle choices and the state of the environment, people nowadays suffer from a wide range of ailments. Consequently, early illness prediction becomes a critical obligation. For doctors, however, making a meaningful prognosis based only on symptoms becomes too difficult. Predicting illness correctly is the hardest problem. ML is crucial in helping to anticipate illnesses and find treatments in order to solve this issue. Using disease data, MRIs, chest X-rays, and diabetes records, machine learning (ML) assists in diagnosing and treating various conditions. Given the patient's symptoms, we utilize techniques such as SVM, Decision Trees, Random Forest Classifier, Gaussian Bayes and Multinomial Bayes, and Gradient Boost to predict illnesses. Given an X-ray image, we employ CNN models such as VGG for image classification with transfer learning to predict if a person has tuberculosis, pneumonia, or is disease-free. Brain MRIs (Magnetic Resonance Imaging) can be used to predict brain tumors. With the aid of Support Vector Machines, we are able to forecast if the patient has a pituitary tumor, meningioma, glioma, or no tumor at all (SVM). Diabetes is predicted using a number of important variables, including age and body mass index (BMI). Random forest classifiers are what we use to determine if a person has diabetes or not.