Leveraging Flask API and Machine Learning to Forecast Multiple Diseases

Abstract

The study described in this abstract used machine learning to predict several diseases, and it integrated the model into a Flask API. A user-friendly platform for disease prediction based on patient symptoms and medical history was the goal of the paper. A number of strategies were used to optimize the predictive performance once the machine learning model had been trained on a sizable dataset of patient records. The model was made accessible as a web service through the usage of the Flask API, enabling simple deployment and integration into current healthcare systems. The study's findings demonstrated that the model could accurately forecast diseases with a high degree of precision, and the Flask API gave healthcare professionals an easy way to use the model in real-world settings. The study underscores the significance of developing open and user-friendly platforms for healthcare applications and shows the potential of machine learning and online APIs in improving disease detection. Additionally, the study discovered that the model could correctly identify disorders even in the absence of conclusive diagnostic tests.