Key result
Random Forest classifier achieves 99% accuracy for symptom-based disease prediction.
Why the study?
The study was motivated by the growing need for tailored healthcare solutions using machine learning in disease prediction and health monitoring.
Does a machine learning-based predictive analytics system accurately forecast diseases based on input signs and symptoms?
Does a machine learning-based predictive analytics system accurately forecast diseases based on input signs and symptoms?
A Random Forest machine learning model can predict diseases from symptom input with 99% accuracy, potentially aiding in early disease identification and personalized health advice.
Hypothesis-generating for ML symptom-based prediction; leaves open prospective clinical validation before any practice change.
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.
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Nayon et al. (2024) studied Disease prediction. Random Forest Classifier vs. Other machine learning models (SVC, KNN, Gradient Boosting, Logistic Regression, Decision Tree, MultinomialNB) was evaluated on Accuracy. A Random Forest classifier for personalized health monitoring and disease prediction based on symptom input achieved a maximum accuracy of 99%, with an F1 score of 0.990.
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