Advancing Prenatal Diagnostics: A Novel Approach Using Machine Learning to Assess Fetal Well-being
Pranav Khot
, Dr. S.K Singh , Rimsy Dua , Vidhi Shukla
Machine Learning, Prenatal Healthcare Innovation, Predictive Analytics in Obstetrics, Fetal Monitoring Technologies, Ensemble Machine Learning Models, Cardiotocography Analysis, Data-Driven Health Assessments, Fetal Outcome Prediction, Technological Advancements in Maternal Care
Advancements in prenatal care are increasingly leveraging machine learning (ML) technologies to redefine the predictive accuracy of fetal health evaluations. This paper presents an innovative ML-driven approach for the early detection and prediction of fetal health issues, leveraging a rich dataset from cardiotocographic recordings. By applying a suite of sophisticated ML algorithms, including but not limited to Support Vector Machines (SVM), Random Forest (RF), CNN, LSTM and state-of-the-art ensemble models such as XGBoost, AdaBoost and LightGBM, our study offers a ground-breaking framework for analysing fetal heart rate patterns and uterine contractions. Through rigorous training, validation, and the strategic handling of imbalanced medical data, our models demonstrate unparalleled robustness and predictive power. Our findings reveal that ensemble ML methods, with their nuanced analysis capabilities, significantly surpass traditional fetal health monitoring techniques, providing deeper insights into potential fetal distress and broader health implications. By bridging the gap between conventional medical practices and the forefront of technological innovation, this research not only contributes to the transformative potential of ML in prenatal care but also envisages a future where pregnancies are monitored with enhanced precision, ensuring optimal health outcomes for both mothers and their unborn children. This study underscores the vital role of ML in pioneering a new paradigm in prenatal healthcare, offering a promising outlook for early interventions and the holistic well-being of fetuses. Through the lens of advanced data analytics and ML, we advocate for a tech-enabled revolution in maternal-fetal medicine, paving the way for safer pregnancies and the well-being of future generations.
"Advancing Prenatal Diagnostics: A Novel Approach Using Machine Learning to Assess Fetal Well-being", TIJER - TIJER - INTERNATIONAL RESEARCH JOURNAL (www.TIJER.org), ISSN:2349-9249, Vol.11, Issue 2, page no.a768-a777, February-2024, Available :https://tijer.org/TIJER/papers/TIJER2402097.pdf
Volume 11
Issue 2,
February-2024
Pages : a768-a777
Paper Reg. ID: TIJER_151339
Published Paper Id: TIJER2402097
Downloads: 000301
Research Area: Science and Technology
Country: MUMBAI, MAHARASHTRA, India
ISSN: 2349-9249 | IMPACT FACTOR: 8.57 Calculated By Google Scholar | ESTD YEAR: 2014
An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.57 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator
Publisher: TIJER (IJ Publication) Janvi Wave
