A Modified Aquila-Based Optimized XGBoost Framework for Detecting Probable Seizure Status in Neonates.

Electroencephalography (EEG) is increasingly being used in pediatric neurology and provides opportunities to diagnose various brain illnesses more accurately and precisely. It is thought to be one of the most effective tools for identifying newborn seizures, especially in Neonatal Intensive Care Units (NICUs). However, EEG interpretation is time-consuming and requires specialists with extensive training. It can be challenging and time-consuming to distinguish between seizures since they might have a wide range of clinical characteristics and etiologies. Technological advancements such as the Machine Learning (ML) approach for the rapid and automated diagnosis of newborn seizures have increased in recent years. This work proposes a novel optimized ML framework to eradicate the constraints of conventional seizure detection techniques. Moreover, we modified a novel meta-heuristic optimization algorithm (MHOA), named Aquila Optimization (AO), to develop an optimized model to make our proposed framework more efficient and robust. To conduct a comparison-based study, we also examined the performance of our optimized model with that of other classifiers, including the Decision Tree (DT), Random Forest (RF), and Gradient Boosting Classifier (GBC). This framework was validated on a public dataset of Helsinki University Hospital, where EEG signals were collected from 79 neonates. Our proposed model acquired encouraging results showing a 93.38% Accuracy Score, 93.9% Area Under the Curve (AUC), 92.72% F1 score, 65.17% Kappa, 93.38% sensitivity, and 77.52% specificity. Thus, it outperforms most of the present shallow ML architectures by showing improvements in accuracy and AUC scores. We believe that these results indicate a major advance in the detection of newborn seizures, which will benefit the medical community by increasing the reliability of the detection process.

HGSORF: Henry Gas Solubility Optimization-based Random Forest for C-Section prediction and XAI-based cause analysis.

A stable predictive model is essential for forecasting the chances of cesarean or C-section (CS) delivery, as unnecessary CS delivery can adversely affect neonatal, maternal, and pediatric morbidity and mortality, and can incur significant financial burdens. Limited state-of-the-art machine learning models have been applied in this area in recent years, and the current models are insufficient to correctly predict the probability of CS delivery. To alleviate this drawback, we have proposed a Henry gas solubility optimization (HGSO)-based random forest (RF), with an improved objective function, called HGSORF, for the classification of CS and non-CS classes. Real-world CS datasets can be noisy, such as the Pakistan Demographic and Health Survey (PDHS) dataset used in this study. The HGSO can provide fine-tuned hyperparameters of RF by avoiding local minima points. To compare performance, Gaussian Naive Bayes (GNB), linear discriminant analysis (LDA), K-nearest neighbors (KNN), gradient boosting classifier (GBC), and logistic regression (LR) have been considered in this research. The ADAptive SYNthetic (ADASYN) algorithm has been used to balance the model, and the proposed HGSORF has been compared with other classifiers as well as with other studies. The superior performance was achieved by HGSORF with an accuracy of 98.33% for the PDHS dataset. The hyperparameters of RF have also been optimized by using commonly used hyperparameter-optimization algorithms, and the proposed HGSORF provided comparatively better performance. Additionally, to analyze the causes of CS and their significance, the HGSORF is explained locally and globally using eXplainable artificial intelligence (XAI)-based tools such as SHapely Additive exPlanation (SHAP) and Local Interpretable Model-Agnostic Explanations (LIME). A decision support system has been developed as a potential application to support clinical staffs. All pre-trained models and relevant codes are available on: https://github.com/MIrazul29/HGSORF_CSection.

An Improved Machine-Learning Approach for COVID-19 Prediction Using Harris Hawks Optimization and Feature Analysis Using SHAP.

A healthcare monitoring system needs the support of recent technologies such as artificial intelligence (AI), machine learning (ML), and big data, especially during the COVID-19 pandemic. This global pandemic has already taken millions of lives. Both infected and uninfected people have generated big data where AI and ML can use to combat and detect COVID-19 at an early stage. Motivated by this, an improved ML framework for the early detection of this disease is proposed in this paper. The state-of-the-art Harris hawks optimization (HHO) algorithm with an improved objective function is proposed and applied to optimize the hyperparameters of the ML algorithms, namely HHO-based eXtreme gradient boosting (HHOXGB), light gradient boosting (HHOLGB), categorical boosting (HHOCAT), random forest (HHORF) and support vector classifier (HHOSVC). An ensemble technique was applied to these optimized ML models to improve the prediction performance. Our proposed method was applied to publicly available big COVID-19 data and yielded a prediction accuracy of 92.38% using the ensemble model. In contrast, HHOXGB provided the highest accuracy of 92.23% as a single optimized model. The performance of the proposed method was compared with the traditional algorithms and other ML-based methods. In both cases, our proposed method performed better. Furthermore, not only the classification improvement, but also the features are analyzed in terms of feature importance calculated by SHapely adaptive exPlanations (SHAP) values. A graphical user interface is also discussed as a potential tool for nonspecialist users such as clinical staff and nurses. The processed data, trained model, and codes related to this study are available at GitHub.