International Research Journal of Education and Technology

One disease at a time is the focus of many of the machine learning models for healthcare analysis currently in use. This study proposes a system to forecast the diseases using the Flask Application programming interface (API). The approach for examining the diabetics disease prediction was proposed in this work. Many of the machine learning models like Logistic  Regression,  K-Nearest  Neighbors

,Support Vector Machine, Decision Trees, Random Forest, Gradient Boosting Classifier and XG Boost for health care analysis now in use focus on just one disease at a time. Flask API, and machine learning techniques were utilized to implement numerous disease analyses. The model behavior is saved using Python pickling, and the pickle file is loaded when needed using Python unpicking. The significance of this article analysis is that all the factors that contribute to the sickness are considered while analyzing it, making it possible to identify the disease's full range of potential impacts. Experimental results shown the better performance of the system.

Keywords— Diabetic Retinopathy, Machine Learning, Disease Prediction, Healthcare Analysis, Flask API, Logistic Regression, K- Nearest Neighbors, Support Vector Machine, Decision Tree, Random Forest, Gradient Boosting Classifier, XGBoost, Python Pickling, Medical Diagnosis, Predictive Modeling, Multi- Disease Analysis, Model Deployment, Disease Forecasting, Feature Analysis, Healthcare Informatics.