Key result
Machine learning-integrated FECG telemonitoring predicts fetal cardiac anomalies with ~95% accuracy.
Why the study?
Rural communities face barriers accessing healthcare to detect neonatal cardiac risk factors, and traditional hospital-centric care causes delays that could be alleviated through remote digital telemonitoring.
Does a machine learning integrated FECG telemonitoring system using LOA improve classification accuracy and reduce latency compared to existing systems?
Does a machine learning integrated FECG telemonitoring system using LOA improve classification accuracy and reduce latency compared to existing systems?
A novel machine learning integrated fetal ECG telemonitoring system using the Lion Optimization Algorithm achieved 95.2% accuracy, offering a potential tool for remote fetal monitoring.
Answers are grounded in this paper and cite their sources.
Requires prospective validation before use; leaves open the role of ML-integrated FECG telemonitoring in practice.
According to data gathered from Indian Pediatrics 2018, out of the 200,000 newborns born with Coronary Heart Disease (CHD) annually, one-fifth had severe abnormalities as a result of carelessness and failure to recognize risk factors during the neonatal stage of the baby’s birth. Additionally, 35% of Indians live in urban areas while 65% of people live in rural areas. It can be difficult for rural communities to access quality healthcare facilities. Their ability to acquire the care they need to lower the morbidity rate of the babies will be hampered by the lack of resources, understanding of health issues, and ability to pay for expensive medical care. The length of time needed to make appointments, wait in queue and drive far to see a doctor leads to a number of issues with the hospital-centric system. Long-term hospital stays are made easier for patients by the ability to consult medical specialists remotely thanks to the digitization of healthcare services. Proposed work uses a mobile application to integrate a machine learning prediction model such as Lion Optimization Algorithm into a hardware framework with the help of IoT. To reduce the possibility of anomalies in the sensor data or erroneous triggering, sensor data validation was carried out. A sophisticated smartphone application was created to report the sensor readings and realistically monitor the patient’s data by examining specific health markers through hybrid modulation techniques. With the sharing of the human body’s vital statistics, this application assists in the rapid detection of sickness without requiring direct human participation. The software helps physicians immediately estimate their risk of developing cardiovascular disease. The application predicts cardiac disease with a 95.2% accuracy rate.
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Preethi et al. (2026) studied Fetal cardiac anomalies (n=60). Machine learning integrated FECG telemonitoring system with Lion Optimization Algorithm vs. Existing telemonitoring systems was evaluated on Classification accuracy for predicting cardiac disease. The machine learning integrated FECG telemonitoring system using the Lion Optimization Algorithm predicted fetal cardiac anomalies with a 95.2% accuracy rate.
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