Heart disease (HD) is the prevailing reason behind deaths among the world-wide population. Early prediction of heart diseases can save lives. Predicting cardiovascular or heart disease in advance, a person can be warned beforehand, and the death can be prevented in turn. Machine learning (ML) ...
K. Performance evaluation of different machine learning techniques for prediction of heart disease. Neural Comput. Appl. 29, 685–693 (2018). Article MATH Google Scholar Baratloo, A., Hosseini, M., Negida, A. & El Ashal, G. Part 1: simple definition and calculation of accuracy, ...
This paper presents research for the design and creation of a fuzzy logic-based expert system for the prognosis and diagnosis of heart disease that is ... ML Ali,MS Sadi,MO Goni - 《Plos One》 被引量: 0发表: 2024年 Expert System for Diagnosing Disease Symptoms of Rice Pests Using the ...
The heart rate (maximal heart rate: 186 ± 6 beats min−1), rating of perceived exertion (19 ± 1), and V˙O2max (39.0 ± 7.1 mL kg−1 min−1) of 150 healthy men (age: 20 ± 2 years; height: 175.0 ± 6.0 cm; weight: 73.6 ± 10.7 kg; ...
Heart failure ML: Machine learning RF: Random forest XGBoost: Exterme gradient boosting CatBoost: Categorical boosting LightGBM: Light gradient boosting machine AUC: Area under the curve LOS: Length of hospital stay IQR: Interquartile range SHAP: Shapley additive explanations EHR: Elect...
The diseases heart and diabetes are the foremost cause of higher death rates for people. The dataset contains target features for the diagnosis of heart disease. This work finds the target variable for diabetic disease by comparing the patient's blood sugars to normal levels. Blood pressure, ...
We also found that NT-proBNP > 330 pg/ml is an independent predictor of the occurrence of AEs following TEVAR in patients with TBAD. Although TBAD does not directly affect the ascending aorta, it can still cause malperfusion of the coronary artery. The reasons are as follow: (i) Th...
Next, an ML model was developed to predict PTSD. For this calculation, a systematic approach was undertaken using Python (v. 3.9.12) to enable the prediction equations. The four clusters of the instrument and the sociodemographic variables were introduced to construct the model. The total of ea...
As most patients being treated for cancer would have an initial oncology document, a model using this data could be widely used, no matter what other data a cancer centre records. Using ML to predict outcomes from documents falls under the branch of artificial intelligence called Natural Language...
Longitudinal risk prediction of chronic kidney disease in diabetic patients using a temporal-enhanced gradient boosting machine: retrospective cohort study. JMIR Med. Inf. 8, e15510 (2020). Article Google Scholar Segar, M. W. et al. Machine learning to predict the risk of incident heart ...