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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) Indonesian Journal of Electrical Engineering and Computer Science
S. H., Manjula
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Computer Science & IT Engineering

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Enhancing sepsis detection using feed-forward neural networks with hyperparameter tuning techniques N., Smitha; R., Tanuja; S. H., Manjula
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 2: April 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i2.pp1252-1259

Abstract

This paper investigates the use of feed-forward neural networks for sepsis detection, emphasizing class imbalance mitigation and hyperparameter optimization. Leveraging random oversampling, synthetic minority over-sampling technique (SMOTE), and random sampling techniques, we address class imbalance, significantly improving feed-forward neural network performance. The resulting model achieves an impressive 83% accuracy on the test set, with notable enhancements in precision, recall, and F1-score for the positive class. Hyperparameter tuning using RandomizedSearchCV identifies optimal parameters, including an alpha value of 0.01 and the logistic activation function, leading to a remarkable 57.5% test accuracy. GridSearchCV also contributes to model refinement, albeit with a slightly lower test accuracy of 51.5%. These findings underscore the importance of robust hyperparameter tuning methods in optimizing feed-forward neural network models for imbalanced datasets, particularly in sepsis detection. The insights gained hold promise for the development of more accurate diagnostic tools, ultimately improving patient outcomes in clinical practice.
Cardio meta-stack: a meta-classifier ensemble for enhanced cardiovascular disease prognosis S., Swetha; Zolgikar, Sneha; S. H., Manjula; K. R., Venugopal
Indonesian Journal of Electrical Engineering and Computer Science Vol 40, No 3: December 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v40.i3.pp1630-1637

Abstract

Cardiovascular diseases (CVDs) remain a significant global health concern, necessitating effective preventive measures and early diagnosis to reduce mortality rates. Leveraging machine learning models to identify risk factors holds great promise, especially in cardiology. This study introduces a robust methodology for prognosing cardiac illnesses based on patient-specific factors. By integrating five publicly available datasets from the UCI Repository and employing Feature Importance techniques for optimal risk factor selection, the proposed approach enhances prediction accuracy. Furthermore, the inclusion of the density-based spatial clustering of applications with noise (DBSCAN) algorithm assists in noise detection and removal, thereby improving model precision. The proposed Cardio MetaStack model, coupled with a stacking classifier ensemble, achieved an accuracy of 94.91%, surpassing that of traditional algorithms such as XGBoost 90.45%, demonstrating its efficacy in heart disease prediction.
Co-Authors K. R., Venugopal N., Smitha R., Tanuja S., Swetha Zolgikar, Sneha