• N. GOWTHAM ABHISHEK
• V. A. TANMAI
• KONDA KARTHIK SRI DATTA
• Mr. B. VENKATESHWARLU

The United Nations estimates around 140 million childbirths every year and around 2.4 - 3 million deaths. However, this number can seem very small in comparison; all these deaths could have been avoided with prior information and proper care during gestation. An unborn baby is called a fetus; most humans' gestation lasts around nine months. The mother and the child’s health have to be monitored regularly to keep track of various characteristics that affect the fetus's health and development. Using the traditional methods, we have specialized procedures to determine if the fetus is normal or not, which requires medical personnel to be trained extensively, which is highly expensive as we have to train the professional, which is time-consuming. The development of Machine Learning Models based on features extracted from CTG is an alternative approach to the conventional methodology, as domain expertise is required for the conventional method. The aim is to develop a machine learning model that is efficient and accurate at predicting the fetal health state based on the Cardiotocogram (CTG) data. The models will be trained on the data that has been carefully classified according to the fetal health state by the doctors. After the model has been trained, we can make predictions on the new data using these models.

FETAL HEALTH CLASSIFICATION USING AI & ML

"FETAL HEALTH CLASSIFICATION USING AI & ML", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.11, Issue 4, page no.d291-d296, April-2024, Available :http://www.jetir.org/papers/JETIR2404341.pdf

"FETAL HEALTH CLASSIFICATION USING AI & ML", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.11, Issue 4, page no. ppd291-d296, April-2024, Available at : http://www.jetir.org/papers/JETIR2404341.pdf