Sufian Md Abu, Hamzi Wahiba, Hamzi Boumediene, Sagar A S M Sharifuzzaman, Rahman Mustafizur, Varadarajan Jayasree, Hanumanthu Mahesh, Azad Md Abul Kalam
IVR Low-Carbon Research Institute, Chang'an University, Xi'an 710018, China.
School of Computing and Mathematical Sciences, University of Leicester, Leicester LE1 7RH, UK.
Diagnostics (Basel). 2024 Apr 28;14(9):920. doi: 10.3390/diagnostics14090920.
Early pregnancy loss (EPL) is a prevalent health concern with significant implications globally for gestational health. This research leverages machine learning to enhance the prediction of EPL and to differentiate between typical pregnancies and those at elevated risk during the initial trimester. We employed different machine learning methodologies, from conventional models to more advanced ones such as deep learning and multilayer perceptron models. Results from both classical and advanced machine learning models were evaluated using confusion matrices, cross-validation techniques, and analysis of feature significance to obtain correct decisions among algorithmic strategies on early pregnancy loss and the vitamin D serum connection in gestational health. The results demonstrated that machine learning is a powerful tool for accurately predicting EPL, with advanced models such as deep learning and multilayer perceptron outperforming classical ones. Linear discriminant analysis and quadratic discriminant analysis algorithms were shown to have 98 % accuracy in predicting pregnancy loss outcomes. Key determinants of EPL were identified, including levels of maternal serum vitamin D. In addition, prior pregnancy outcomes and maternal age are crucial factors in gestational health. This study's findings highlight the potential of machine learning in enhancing predictions related to EPL that can contribute to improved gestational health outcomes for mothers and infants.
早期妊娠丢失（EPL）是一个普遍存在的健康问题，在全球范围内对妊娠健康有着重大影响。本研究利用机器学习来加强对EPL的预测，并区分正常妊娠和孕早期高风险妊娠。我们采用了不同的机器学习方法，从传统模型到更先进的模型，如深度学习和多层感知器模型。使用混淆矩阵、交叉验证技术以及特征重要性分析对经典和先进机器学习模型的结果进行评估，以便在早期妊娠丢失及妊娠健康中维生素D血清关联的算法策略中做出正确决策。结果表明，机器学习是准确预测EPL的有力工具，深度学习和多层感知器等先进模型优于经典模型。线性判别分析和二次判别分析算法在预测妊娠丢失结局方面显示出98%的准确率。确定了EPL的关键决定因素，包括母体血清维生素D水平。此外，既往妊娠结局和产妇年龄是妊娠健康的关键因素。本研究结果突出了机器学习在加强与EPL相关预测方面的潜力，这有助于改善母婴的妊娠健康结局。
