Machine Learning-Based Personalized Health Monitoring and Predictive Analytics

The use of machine learning in disease prediction and health monitoring is a result of the growing need for tailored healthcare solutions. In this paper, a web-based platform for symptom input is used to offer a machine learning-based personalized health monitoring and predictive analytics system that helps people recognize possible health problems. To forecast diseases with respect to input signs and symptoms, the technique makes the most of several types of machine learning models, such as Support Vector Classifier (SVC), K-Nearest Neighbors (KNN), Random Forest Classifier, Gradient Boosting Classifier, Logistic Regression, Decision Tree, and Multinomial Naive Bayes(MultinomialNB).Among them, Random Forest achieved the maximum accuracy 99%, with F1 Score 0.990, Precision score 0.990, and recall score 0.990. By including tailored advice on prescription drugs, safety precautions, exercise regimens, and food plans, the forecasts are further improved. In order to guarantee precise disease detection and to provide individualized, appropriate and timely health advice, the models were trained on a variety of health datasets. This strategy lessens the load on healthcare systems by empowering consumers to make educated decisions about their health management and by facilitating early disease identification.