A Self-Care Prediction Model for Children with Disability Based on Genetic Algorithm and Extreme Gradient Boosting

Syafrudin, Muhammad; Alfian, Ganjar; Fitriyani, Norma Latif; Anshari, Muhammad; Hadibarata, Tony; Fatwanto, Agung; Rhee, Jongtae

doi:10.3390/math8091590

Cited by 12 publications

(5 citation statements)

References 42 publications

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“…In our study, we employed an evolutionary-based strategy for feature selection [ 43 ]. Known for its proficiency in implementing search methodologies, a genetic algorithm (GA) was paired with a 5-fold cross-validation strategy.…”

Section: Methodsmentioning

confidence: 99%

From Admission to Discharge: Predicting National Institutes of Health Stroke Scale Progression in Stroke Patients Using Biomarkers and Explainable Machine Learning

Gkantzios,

Kokkotis,

Tsiptsios

et al. 2023

JPM

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As a result of social progress and improved living conditions, which have contributed to a prolonged life expectancy, the prevalence of strokes has increased and has become a significant phenomenon. Despite the available stroke treatment options, patients frequently suffer from significant disability after a stroke. Initial stroke severity is a significant predictor of functional dependence and mortality following an acute stroke. The current study aims to collect and analyze data from the hyperacute and acute phases of stroke, as well as from the medical history of the patients, in order to develop an explainable machine learning model for predicting stroke-related neurological deficits at discharge, as measured by the National Institutes of Health Stroke Scale (NIHSS). More specifically, we approached the data as a binary task problem: improvement of NIHSS progression vs. worsening of NIHSS progression at discharge, using baseline data within the first 72 h. For feature selection, a genetic algorithm was applied. Using various classifiers, we found that the best scores were achieved from the Random Forest (RF) classifier at the 15 most informative biomarkers and parameters for the binary task of the prediction of NIHSS score progression. RF achieved 91.13% accuracy, 91.13% recall, 90.89% precision, 91.00% f1-score, 8.87% FNrate and 4.59% FPrate. Those biomarkers are: age, gender, NIHSS upon admission, intubation, history of hypertension and smoking, the initial diagnosis of hypertension, diabetes, dyslipidemia and atrial fibrillation, high-density lipoprotein (HDL) levels, stroke localization, systolic blood pressure levels, as well as erythrocyte sedimentation rate (ESR) levels upon admission and the onset of respiratory infection. The SHapley Additive exPlanations (SHAP) model interpreted the impact of the selected features on the model output. Our findings suggest that the aforementioned variables may play a significant role in determining stroke patients’ NIHSS progression from the time of admission until their discharge.

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Section: Methodsmentioning

confidence: 99%

From Admission to Discharge: Predicting National Institutes of Health Stroke Scale Progression in Stroke Patients Using Biomarkers and Explainable Machine Learning

Gkantzios,

Kokkotis,

Tsiptsios

et al. 2023

JPM

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“…Web-based diagnostics have been widely utilized by researchers to detect risks and facilitate decision making in a range of contexts, including the prediction of chronic disease [33,34], violent behavior [35], self-care [36], and preventive medicine [37]. Therefore, the objective of our work is to design and implement a web-based cancer screening tool that will aid the medical team in making screening decisions.…”

Section: Management Implicationsmentioning

confidence: 99%

Predicting Breast Cancer from Risk Factors Using SVM and Extra-Trees-Based Feature Selection Method

et al. 2022

Self Cite

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Developing a prediction model from risk factors can provide an efficient method to recognize breast cancer. Machine learning (ML) algorithms have been applied to increase the efficiency of diagnosis at the early stage. This paper studies a support vector machine (SVM) combined with an extremely randomized trees classifier (extra-trees) to provide a diagnosis of breast cancer at the early stage based on risk factors. The extra-trees classifier was used to remove irrelevant features, while SVM was utilized to diagnose the breast cancer status. A breast cancer dataset consisting of 116 subjects was utilized by machine learning models to predict breast cancer, while the stratified 10-fold cross-validation was employed for the model evaluation. Our proposed combined SVM and extra-trees model reached the highest accuracy up to 80.23%, which was significantly better than the other ML model. The experimental results demonstrated that by applying extra-trees-based feature selection, the average ML prediction accuracy was improved by up to 7.29% as contrasted to ML without the feature selection method. Our proposed model is expected to increase the efficiency of breast cancer diagnosis based on risk factors. In addition, we presented the proposed prediction model that could be employed for web-based breast cancer prediction. The proposed model is expected to improve diagnostic decision-support systems by predicting breast cancer disease accurately.

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“…The prediction of chronic illness [33], [42], [43], self-care [44], and preventive medicine [45] are just a few areas where previous studies have shown how machine learning models might be integrated into web-based systems to help with decision-making. Therefore, the goal of our study is to develop and deploy a web-based system for forecasting blood glucose levels, which will help the patient obtain future blood glucose readings.…”

Section: Practical Implicationmentioning

confidence: 99%

Utilizing deep neural network for web-based blood glucose level prediction system

Alfian

Saputra

Subekti

et al. 2023

IJEECS

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Machine learning algorithms can be used to forecast future blood glucose (BG) levels for diabetes patients, according to recent studies. In this study, dataset from continuous glucose monitoring (CGM) system was used as the sole input for the machine learning models. To forecast blood glucose levels 15, 30, and 45 minutes in the future, we suggested deep neural network (DNN) and tested it on 7 patients with type 1 diabetes (T1D). The suggested prediction model was evaluated against a variety of machine learning models, such as k-nearest neighbor (KNN), support vector regression (SVR), decision tree (DT), adaptive boosting (AdaBoost), random forest (RF), and eXtreme gradient boosting (XGBoost). The experimental findings demonstrated that the proposed DNN model outperformed all other models, with average root mean square errors (RMSEs) of 17.295, 25.940, and 35.146 mg/dL over prediction horizons (PHs) of 15, 30, and 45 minutes, respectively. Additionally, we have included the suggested prediction model in web-based blood glucose level prediction tools. By using this web-based system, patients may readily acquire their future blood glucose levels, allowing for the generation of preventative alarms prior to crucial hypoglycemia or hyperglycemic situations.

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A Self-Care Prediction Model for Children with Disability Based on Genetic Algorithm and Extreme Gradient Boosting

Cited by 12 publications

References 42 publications

From Admission to Discharge: Predicting National Institutes of Health Stroke Scale Progression in Stroke Patients Using Biomarkers and Explainable Machine Learning

From Admission to Discharge: Predicting National Institutes of Health Stroke Scale Progression in Stroke Patients Using Biomarkers and Explainable Machine Learning

Predicting Breast Cancer from Risk Factors Using SVM and Extra-Trees-Based Feature Selection Method

Utilizing deep neural network for web-based blood glucose level prediction system

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