Birth Weight Estimation
Fetal Birth Weight Estimation in High-risk Pregnancies through Machine Learning Techniques
Price : 7000
The low weight of fetus at birth is considered one of the most critical problems in pregnancy care, affecting the newborn’s health and leading it to death in more severe cases. This condition is responsible for the high infant mortality rates worldwide. In health, artificial intelligence techniques, especially those based on machine learning (ML), can early predict problems related to the fetus’ health state during entire gestation, including at birth. Hence, this project proposes an analysis of several ML techniques capable of predicting whether the fetus will born small for its gestational age. The importance of the early diagnosis of problems related to fetal development relies on the possibility of an increase in the gestation days through timely intervention. Such intervention would allow an improvement in fetal weight at birth, associated with a decrease in neonatal morbidity and mortality.
• System: Pentium IV 2.4 GHz.
• Hard Disk: 500 GB.
• Ram: 4 GB.
• Any desktop / Laptop system with above configuration or higher level.
• Operating system : Windows XP / 7
• Coding Language :Python
• Interpreter :Python 3.6
• IDE : Python IDE
• ML APIS :Sklearn, numpy, pandas, matplotlib, ML algorithms
Current standards for ultrasound assessment of fetal growth can lead to misclassification of up to 15% of fetuses, considering them SGA. Growth limitation represents a sign of severe health problem, often resulting from the fetus not receiving enough nutrients or oxygen in the uterus. The understanding of many aspects of fetal growth and path physiology of its restriction is still weak. As clinical proposals, several models of ultrasound techniques have been developed. However, an accurate method for the disease diagnosis has not yet been found. It is known that the more initial the growth restriction, the greater the severity. ML techniques represent an essential tool to assist specialists in the early identification of this disturbance. We proposed a system using machine learning techniques and algorithms to predict fetal birth weight with good accuracy.