Exploring the Performances of Stacking Classifier in Predicting Patients Having Stroke

Hasan, Tasnimul; Nishat, Mirza Muntasir; Faisal, Fahim; Islam, Abrar; Mehadi, Abdullah Al; Nasrullah, Sarker Md.; Islam, Mohammad Rakibul

doi:10.1109/nics54270.2021.9701526

Cited by 21 publications

(2 citation statements)

References 28 publications

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“…This research shows that the stacking classifier model has a high accuracy of about 95%, which can help in early stroke detection and appropriate treatment. These findings encourage the use of machine learning models for more accurate medical diagnosis and early treatment [23].…”

Section: Literature Reviewmentioning

confidence: 81%

Stacking classifier method for prediction of human body performance

Octavianto,

Wibowo

2024

IJEECS

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<p>A healthy body is the capital of success and supports human activities. To maintain health, humans need to avoid disease. A healthy life is everyone’s dream and should start early. Busy activities often hinder a healthy lifestyle. Nonetheless, it is important for every individual to lead a healthy lifestyle. Human activities determine health and the implementation of a healthy life. One method that can perform classification with machine learning is extreme gradient boosting (XGBoost). XGBoost is one of the techniques in machine learning for regression analysis and classification based on gradient boosting decision tree (GBDT). By using gradient descent to minimize the error when creating a new model, the algorithm is called gradient boosting. In determining a classification starting from determining the model to the results, usually only using one algorithm method, and combining other methods together with the method is an algorithm called random forest classifier. Among these merging methods are, stacking classifier, voting classifier, and bagging classifier. The conclusion obtained from the results of this research is that the test results show that the stacking classifier achieves the highest accuracy of 76.07%, making it the best method in this research. And the stacking classifier has a precision of 76.96%, recall of 75.83%, and F1-score of 75.81%. This shows that the model has a good balance between the ability to provide true positive results and the ability to recover positive data.</p>

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Section: Literature Reviewmentioning

confidence: 81%

Stacking classifier method for prediction of human body performance

Octavianto,

Wibowo

2024

IJEECS

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“…It has significant predictive ability for this type of problem and has been extensively used in recent years in a variety of sophisticated healthcare systems [16][17][18][19][20]. Moreover, it has lately been shown to be incredibly effective in the healthcare arena [21][22][23][24][25][26]. In the case of BMT, an ML-based support system can play an important role by predicting the patient's survival after treatment and assisting in the necessary preparations prior to therapy.…”

Section: Introductionmentioning

confidence: 99%

Survival Prediction of Children Undergoing Hematopoietic Stem Cell Transplantation Using Different Machine Learning Classifiers by Performing Chi-Square Test and Hyperparameter Optimization: A Retrospective Analysis

Ratul

Habiba

Nishat

et al. 2022

Computational and Mathematical Methods in Medicine

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Bone marrow transplant (BMT) is an effective surgical treatment for bone marrow-related disorders. However, several associated risk factors can impair long-term survival after BMT. Machine learning (ML) technologies have been proven useful in survival prediction of BMT receivers along with the influences that limit their resilience. In this study, an efficient classification model predicting the survival of children undergoing BMT is presented using a public dataset. Several supervised ML methods were investigated in this regard with an 80-20 train-test split ratio. To ensure prediction with minimal time and resources, only the top 11 out of the 59 dataset features were considered using Chi-square feature selection method. Furthermore, hyperparameter optimization (HPO) using the grid search cross-validation (GSCV) technique was adopted to increase the accuracy of prediction. Four experiments were conducted utilizing a combination of default and optimized hyperparameters on the original and reduced datasets. Our investigation revealed that the top 11 features of HPO had the same prediction accuracy (94.73%) as the entire dataset with default parameters, however, requiring minimal time and resources. Hence, the proposed approach may aid in the development of a computer-aided diagnostic system with satisfactory accuracy and minimal computation time by utilizing medical data records.

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