In the present era of the medical field, the mortality rate of the infant increased by 1/3 percentage in a year. Even though we have modernized and have great expertise in the medical domain, we failed to control the infantile mortality rate. So continuous monitoring of the fetus during pregnancy is important. If there is any complication in the growth of the fetus, then the patient is put into the appropriate examination and medication proposed by the physician. In this paper, we propose methods to control the infant mortality rate in the early stage of pregnancy. Fetal heart rate of 2126 patients was collected and developed as datasets. Then with these datasets, we developed machine learning classifier models to classify the normal, suspect, and pathologic cases using the Decision tree, Naive Bayes, Random forest, and K-nearest neighbors. We divided the datasets into training dataset and testing dataset. Then the base classifier model was created using training datasets, and then the same models were verified by appending the test datasets. We improved the techniques and efficacy of the base classifiers by ensemble methods such as bagging and boosting. Finally, the datasets were classified as normal, suspect, and pathological cases with an improved accuracy of 96.617% with the help of the random forest classifier.

在当今医学界，婴儿死亡率每年增加1/3%。尽管我们已经现代化并在医疗领域拥有丰富的专业知识，但我们未能控制婴儿死亡率。因此，在怀孕期间持续监测胎儿很重要。如果胎儿的生长有任何并发​​症，则患者将接受医生建议的适当检查和药物治疗。在本文中，我们提出了控制妊娠早期婴儿死亡率的方法。收集并开发了 2126 名患者的胎心率数据集。然后利用这些数据集，我们开发了机器学习分类器模型，使用决策树、朴素贝叶斯、随机森林和 K 最近邻对正常、可疑和病理病例进行分类。我们将数据集分为训练数据集和测试数据集。然后使用训练数据集创建基本分类器模型，然后通过附加测试数据集验证相同的模型。我们通过 bagging 和 boosting 等集成方法改进了基础分类器的技术和功效。最后，在随机森林分类器的帮助下，数据集被分类为正常、疑似和病理病例，准确率提高到 96.617%。