Fetal Health Classification and Birth Weight Estimation Using Machine Learning


Authors : Shreeya R Hegde; Sinchana S; Supriya P Nadgir; Vinamratha R Jagirdar

Volume/Issue : Volume 9 - 2024, Issue 10 - October

Google Scholar : https://tinyurl.com/5axx8tcy

Scribd : https://tinyurl.com/4t6nekhy

DOI : https://doi.org/10.38124/ijisrt/IJISRT24OCT1526

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Abstract : This paper addresses fetal health prediction via Cardiotocography (CTG) data analysis, utilizing models like Logistic Regression, SVM, and boosting algorithms. Feature selection methods such as PCA and LDA are employed, along with SMOTE for dataset balancing. CatBoost model emerges as superior with 99% accuracy. Fetal weight prediction remains challenging, tackled through machine learning algorithms incorporating parameters like gestational period and maternal factors. Models like Random Forest and Adaboost are employed, with RMSE analysis guiding their combination for improved prediction accuracy. Overall, the paper emphasizes leveraging ML for fetal health classification and birth weight prediction.

Keywords : Fetal Health, Cardiotocography, Logistic Regression, SVM, XGBoost, CatBoost, SMOTE Technique, Fetal Birth Weight.

References :

  1. Unveiling the Unborn: Advancing Fetal Health Classification through Machine Learning(2023): - Sujith K Mandala
  2. Early Diagnosis and Classification of Fetal Health Status from a Fetal Cardiotocography Dataset Using Ensemble Learning(2023):- Adem Kuzu and Yunus Santur
  3. Fetal Health Classification from Cardiotocograph for Both Stages of Labor—A Soft-Computing Based Approach(2023):-Sahana Das, Himadri Mukherjee, Kaushik Roy and Chanchal Kumar Saha
  4. Using Machine Learning to Classify Human Fetal Health and Analyze Feature Importance(2023):-Yiqiao Yin  and Yash Bingi
  5. Machine learning applied in maternal and fetal health: a narrative review(2023):
  6. -Daniela Mennickent,André Rodríguez, Ma Opazo, Claudia Riedel,Erica Castro, Alma Eriz-Salinas, Javiera Appel-Rubio, Claudio Aguayo, Alicia Damiano, Enrique Guzmán, Gutiérrez
  7. A Review on Deep-Learning Algorithms for Fetal Ultrasound-Image Analysis(2022):-Maria Chiara Fiorentino, Francesca Pia Villani, Mariachiara Di Cosmo, Emanuele Frontoni, and Sara Moccia
  8. Comparative Analysis of Different Efficient Machine Learning Methods for Fetal Health Classification(2022):-Md Takbir Alam,1 Md Ashibul Islam Khan,Nahian Nakiba Dola, Tahia Tazin , Mohammad Monirujjaman Khan
  9. Estimation of fetal weight by clinical method and its comparison with ultrasonography and its correlation with actual birth weight in term singleton pregnancy(2022):-Kantamani Pavithra, Kanaparthy Priyadarshini,  Y Annapoorna

This paper addresses fetal health prediction via Cardiotocography (CTG) data analysis, utilizing models like Logistic Regression, SVM, and boosting algorithms. Feature selection methods such as PCA and LDA are employed, along with SMOTE for dataset balancing. CatBoost model emerges as superior with 99% accuracy. Fetal weight prediction remains challenging, tackled through machine learning algorithms incorporating parameters like gestational period and maternal factors. Models like Random Forest and Adaboost are employed, with RMSE analysis guiding their combination for improved prediction accuracy. Overall, the paper emphasizes leveraging ML for fetal health classification and birth weight prediction.

Keywords : Fetal Health, Cardiotocography, Logistic Regression, SVM, XGBoost, CatBoost, SMOTE Technique, Fetal Birth Weight.

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