An Experimental Study of Diversity of Diabetes Disease Features by Bagging and Boosting Ensemble Method with Rule Based Machine Learning Classifier Algorithms

Yadav, Dhyan Chandra; Pal, Saurabh

doi:10.1007/s42979-020-00446-y

Cited by 12 publications

(3 citation statements)

References 29 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The most important attributes establish a link between two categorical variables, specifically, a period, which is a relationship between observed and predicted frequency. For diabetic data, the Chi-Square technique is applied to calculate the attribute scores [ 23 ]. A cross-validation with 10-fold was used.…”

Section: Data Preprocessing and Methodologymentioning

confidence: 99%

An Ensemble Approach to Predict Early-Stage Diabetes Risk Using Machine Learning: An Empirical Study

Laila

Mahboob

Khan

et al. 2022

Sensors

View full text Add to dashboard Cite

Diabetes is a long-lasting disease triggered by expanded sugar levels in human blood and can affect various organs if left untreated. It contributes to heart disease, kidney issues, damaged nerves, damaged blood vessels, and blindness. Timely disease prediction can save precious lives and enable healthcare advisors to take care of the conditions. Most diabetic patients know little about the risk factors they face before diagnosis. Nowadays, hospitals deploy basic information systems, which generate vast amounts of data that cannot be converted into proper/useful information and cannot be used to support decision making for clinical purposes. There are different automated techniques available for the earlier prediction of disease. Ensemble learning is a data analysis technique that combines multiple techniques into a single optimal predictive system to evaluate bias and variation, and to improve predictions. Diabetes data, which included 17 variables, were gathered from the UCI repository of various datasets. The predictive models used in this study include AdaBoost, Bagging, and Random Forest, to compare the precision, recall, classification accuracy, and F1-score. Finally, the Random Forest Ensemble Method had the best accuracy (97%), whereas the AdaBoost and Bagging algorithms had lower accuracy, precision, recall, and F1-scores.

show abstract

Section: Data Preprocessing and Methodologymentioning

confidence: 99%

An Ensemble Approach to Predict Early-Stage Diabetes Risk Using Machine Learning: An Empirical Study

Laila

Mahboob

Khan

et al. 2022

Sensors

View full text Add to dashboard Cite

show abstract

“…The computational capacity of these approaches allows for the prediction of diabetes in its early stages, promoting proactive medical care. A variety of machine learning-based algorithmic methods have emerged throughout the medical field, including SVM [75], ANN [76], ELM [77], AdaBoost [78], RF [79], Bagging [80], KNN [81], and DNN [53][82]. This landscape of methodologies reflects the ongoing commitment to advancing diagnostic precision through innovative computational techniques (Table IV).…”

Section: Discussionmentioning

confidence: 99%

Optimizing Predictive Performance: Hyperparameter Tuning in Stacked Multi-Kernel Support Vector Machine Random Forest Models for Diabetes Identification

Saputra,

Ma'arif,

Sunat

2024

Journal of Robotics and Control

View full text Add to dashboard Cite

This study addresses the necessity for more advanced diagnostic tools in managing diabetes, a chronic metabolic disorder that leads to disruptions in glucose, lipid, and protein metabolism caused by insufficient insulin activity. The research investigates the innovative application of machine learning models, specifically Stacked Multi-Kernel Support Vector Machines Random Forest (SMKSVM-RF), to determine their effectiveness in identifying complex patterns in medical data. The innovative ensemble learning method SMKSVM-RF combines the strengths of Support Vector Machines (SVMs) and Random Forests (RFs) to leverage their diversity and complementary features. The SVM component implements multiple kernels to identify unique data patterns, while the RF component consists of an ensemble of decision trees to ensure reliable predictions. Integrating these models into a stacked architecture allows SMKSVM-RF to enhance the overall predictive performance for classification or regression tasks by optimizing their strengths. A significant finding of this study is the introduction of SMKSVM-RF, which displays an impressive 73.37% accuracy rate in the confusion matrix. Additionally, its recall is 71.62%, its precision is 70.13%, and it has a noteworthy F1-Score of 71.34%. This innovative technique shows potential for enhancing current methods and developing into an ideal healthcare system, signifying a noteworthy step forward in diabetes detection. The results emphasize the importance of sophisticated machine learning methods, highlighting how SMKSVM-RF can improve diagnostic precision and aid in the continual advancement of healthcare systems for more effective diabetes management.

show abstract

“…The results showed that bagging with RF achieved higher accuracy. The research of Yadav and Pal [30] applied rule-based classification algorithms on a prepared dataset with three selected algorithms by bagging and boosting ensemble methods and calculated four metrics on a diabetes dataset. The research revealed that bagging exhibited the highest accuracy, namely, 98%.…”

Section: Bagging and Boosting Ensemblesmentioning

confidence: 99%

Early Identification of Risk Factors in Non-Alcoholic Fatty Liver Disease (NAFLD) Using Machine Learning

et al. 2023

View full text Add to dashboard Cite

Liver diseases are a widespread and severe health concern, affecting millions worldwide. Non-alcoholic fatty liver disease (NAFLD) alone affects one-third of the global population, with some Latin American countries seeing rates exceeding 50%. This alarming trend has prompted researchers to explore new methods for identifying those at risk. One promising approach is using Machine Learning Algorithms (MLAs), which can help predict critical factors contributing to liver disease development. Our study examined nine different MLAs across four datasets to determine their effectiveness in predicting this condition. We analyzed each algorithm’s performance using five important metrics: accuracy, precision, recall, f1-score, and roc_auc. Our results showed that these algorithms were highly effective when used individually and as part of an ensemble modeling technique such as bagging or boosting. We identified alanine aminotransferase (ALT), aspartate aminotransferase (AST), alkaline phosphatase (ALP), and albumin as the top four attributes most strongly associated with non-alcoholic fatty liver disease risk across all datasets. Gamma-glutamyl transpeptidase (GGT), hemoglobin, age, and prothrombin time also played significant roles. In conclusion, this research provides valuable insights into how we can better detect and prevent non-alcoholic fatty liver diseases by leveraging advanced machine learning techniques. As such, it represents an exciting opportunity for healthcare professionals seeking more accurate diagnostic tools while improving patient outcomes globally.

show abstract

An Experimental Study of Diversity of Diabetes Disease Features by Bagging and Boosting Ensemble Method with Rule Based Machine Learning Classifier Algorithms

Cited by 12 publications

References 29 publications

An Ensemble Approach to Predict Early-Stage Diabetes Risk Using Machine Learning: An Empirical Study

An Ensemble Approach to Predict Early-Stage Diabetes Risk Using Machine Learning: An Empirical Study

Optimizing Predictive Performance: Hyperparameter Tuning in Stacked Multi-Kernel Support Vector Machine Random Forest Models for Diabetes Identification

Early Identification of Risk Factors in Non-Alcoholic Fatty Liver Disease (NAFLD) Using Machine Learning

Contact Info

Product

Resources

About