| Popis: |
Pregnancy complications significantly impact women and pose potential threats to the developing child’s health. Early identification of these complications is imperative for life-saving interventions. The manual analysis of cardiotocography (CTG) tests, a conventional practice among obstetricians, is both labor-intensive and unreliable. Consequently, the development of efficient fetal health classification models becomes crucial for optimizing medical resources and saving time.This study addresses the imperative for advanced fetal health classification through the application of machine learning (ML) techniques. The objective is to explore, develop, and analyze ML models capable of accurately classifying fetal health based on CTG data. The overarching goal is to enhance diagnostic precision and facilitate timely interventions.Utilizing a freely available cardiotocography data set, despite its relatively small size, the research acknowledges its rich characteristics. Various ML models, including Random Forests, Logistic Regression, Decision Trees, Support Vector Classifiers, Voting Classes, and K-Nearest Neighbors, are deployed on the data set. The analysis involves rigorous training and testing of these models to assess their efficacy in classifying fetal health.The study yields promising outcomes, with the implemented ML models achieving a notable accuracy level of 93%, surpassing previous methods. This underscores the effectiveness of the proposed models in elevating the precision of fetal health classification based on CTG data.The findings advocate for the integration of ML models into routine clinical practices, streamlining fetal health assessments. The study not only underscores the significance of early complication detection but also demonstrates the potential of ML in optimizing medical resource allocation and time efficiency. Further research is warranted to refine and expand ML applications in the context of fetal health assessment, promising advancements in prenatal care. |
