A Short Overview of Computer-Assisted Strategies for Preterm Birth

Preterm birth affects millions of children each year throughout the world. Medical research currently focuses on reducing the effects of preterm rather than avoiding it. The length of the cervix is measured during a transvaginal ultrasound, which is used to diagnose the condition. Due to the complexities of this method and its subjective judgement, approximately 30% of preterm deliveries are incorrectly anticipated. According to current study, machine learning may be a useful technique for assisting in the detection of premature babies. Preterm birth (PTB) in a pregnant woman is the most critical problem in gynaecology and obstetrics, particularly in rural India. To improve the accuracy of learning models, numerous clinical prediction models for PTB have been created in recent years. To the authors' understanding, however, the majority of them have difficulty identifying the most accurate characteristics from the medical dataset in linear time. To create a computer-based model for predicting PTB called the risk prediction conceptual model (RPCM).

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