Women Maternal Risk Prediction using AI | IJCSE Volume 9 – Issue 6 | IJCSE-V9I6P4

maternal mortality and morbidity remain significant global health challenges. Early and accurate identification of at-risk pregnancies is crucial for timely intervention and improved outcomes. This paper explores the application of machine learning (ML) for predicting maternal health risks. We employ a publicly available dataset featuring key physiological and demographic indicators to develop and evaluate three distinct classification models: K-Nearest Neighbours (KNN), Naive Bayes, and Decision Tree. The methodology involves data pre-processing, feature scaling, model training, and performance evaluation. Our results demonstrate that the Decision Tree classifier achieves the highest accuracy in identifying risk levels (low, medium, high). The comparative analysis reveals the Decision Tree’s superior capability in handling the dataset’s characteristics, offering an interpretable and effective model for clinical decision support. This work underscores the potential of ML to augment traditional risk assessment methods, providing a scalable and data-driven tool for healthcare professionals.

This study successfully demonstrated the application of machine learning for maternal health risk prediction. Through a comparative analysis of K- Nearest Neighbours, Naive Bayes, and Decision Tree classifiers, we established that the Decision Tree model provides the best performance, achieving an accuracy of 96.6%. Its ability to model complex relationships and provide interpretable results makes it an ideal candidate for a clinical decision support system. While promising, this work has limitations. The model was trained on a specific dataset and its generalizability should be tested on larger, more diverse populations. Future work should focus on: Exploring more advanced ensemble models like Random Forest and Gradient Boosting, which often build upon the strengths of Decision Trees. Incorporating a wider range of features, including lifestyle factors, socioeconomic data, and past medical history.Developing and deploying a user-friendly application to make this predictive tool accessible to healthcare professionals in real world settings. Ultimately, machine learning stands as a powerful ally in the global effort to improve maternal health outcomes, offering data-driven insights to protect the well-being of mothers everywhere.

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