Table 1 Machine learning implementation on health datasets.
From: Ensemble machine learning framework for predicting maternal health risk during pregnancy
References | Application | Dataset | ML Techniques | Performance |
|---|---|---|---|---|
Sentiment Analysis of COVID-19 Tweets | Tweeter | Adaptive Neuro-Fuzzy Inference System | Accuracy: 0.916 | |
COVID-19 Patient Health Prediction | Novel Corona Virus 2019 Dataset, Kaggle | Random Forest | Accuracy: 0.94 | |
Chronic Diseases Detection Model | Kaggle | Decision Tree | Accuracy: 0.978 | |
Medical Diagnosis | UCI | Multilayer Perceptron | Accuracy: 0.975 | |
Heart Disease Prediction | IEEE Data port | CART | Accuracy: 0.875 | |
Sentiment Analysis of COVID-19 Tweets | ABCML-SA | Accuracy: 0.983 | ||
Heart Disease Detection | Kaggle | Decision Tree | Accuracy: 0.90 | |
Diabetes disease detection | Indian Demographic and Health Dataset | Random Forest | Accuracy: 0.99 | |
Kidney Disease Prediction | Kaggle | LightGBM | Accuracy: 0.99 | |
Cervical Cancer Disease Prediction | UCI | XG Boost | Accuracy: 0.94 | |
Sentiment Classification for Healthcare Tweets | Tweeter | Bagging with KNN | Accuracy: 0.888 |
