An Empirical and Statistical Analysis of Fetal Health Classification Using Different Machine Learning Algorithm

The health of both the mother and the baby is affected by how well the fetus is doing during pregnancy, making it a matter of utmost importance. To achieve the best results possible, it is essential to regularly monitor and intervene when needed. While there are many ways to observe the wellbeing of the fetus in the mother’s womb, using artificial intelligence (AI) has the potential to enhance accuracy, efficiency, and speed when it comes to diagnosing any issues. This study focuses on developing a machine learning-driven system for accurate fetal health classification. The dataset comprises detailed information on the signs and symptoms of pregnant individuals, particularly those at risk or with emerging fetal health issues. Employing a set of ten machine learning models namely Naïve Bayes, Logistic Regression, Decision Tree, Random Forest, KNN, SVM, Gradient Boosting, Linear Discriminant Analysis, Quadratic Discriminant Analysis Light Gradient Boosting Machine (LGBM) along with ensemble-based processes, the Light Gradient Boosting Machine (LGBM) has been identified as a standout performer, accomplishing an accuracy of 96.9%. Furthermore, our exploration demonstrates overall performance like character fashions, signaling promising prospects for sturdy and correct fetal fitness class systems. This study highlights the power of machine learning that could revolutionize prenatal care by identifying fetal health problems early.