Fetal health classification using LightGBM with Grid search based hyper parameter tuning

Background: Fetal health monitoring throughout pregnancy is challenging and complex. Complications in the fetal health not identified at the right time lead to mortality of the fetus as well the pregnant women. Hence, obstetricians check the fetal health state by monitoring the fetal heart rate (FHR). Cardiotocography (CTG) is a technique used by obstetricians to access the physical well-being of fetal during pregnancy. It provides information on the fetal heart rate and uterine respiration, which can assist in determining whether the fetus is normal or suspect or pathology. CTG data has typically been evaluated using machine learning (ML) algorithms in predicting the wellness of the fetal and speeding up the detection process. Methods: In this work, we developed LightGBM with a Grid search-based hyperparameter tuning model to predict fetal health classification. The classification results are analysed quantitatively using the performance measures, namely, precision, Recall, F1-Score, and Accuracy Comparisons were made between different classification models like Logistic Regression, Decision Tree, Random Forest, k-nearest neighbors, Bagging, ADA boosting, XG boosting, and LightGBM, which were trained with the CTG Dataset obtained by the patented fetal monitoring system of 2,216 data points from pregnant women in their third trimester available in the Kaggle dataset. The dataset contains three classes: normal, suspect, and pathology. Our proposed model will give better results in predicting fetal health classification. Results: In this paper, the performance of the proposed algorithm LightGBM is compared and experimented with various Machine learning Techniques namely LR, DT, RF, KNN, Boosting, Ada boosting, and XG Boost and the classification accuracy of the respective algorithms are 84%, 94%, 93%, 88%, 94%, 89%, 96%.The LightGBM achieved a performance of 97% and outperforms the former models. Conclusion: The LightGBM-based fetal health classification has been presented. Ensemble models were applied to the FHR dataset and presented the hybrid algorithm, namely Light GBM, and its application to fetal health classification. LightGBM has advantages that include fast training, improved performance, scale-up capabilities, and lesser memory usage than other ensemble models. The proposed model is more consistent and superior to other considered machine learning models and is suitable for the classification of fetal health based on FHR data. Finally, the outcomes of the multiple methods are compared using the same training and test data in order to verify the efficiency of LightGBM. The model can be further enhanced by making it hybrid by combining the advantages of different models and optimization techniques.