IoT assisted fetal health classification using mother optimization algorithm with deep learning approach on cardiotocogram data.

The adoption of the Internet of Things (IoT) for the application of smart health is an effective method for distributed and intelligent automated diagnosis systems. Fetal movement is a basic index of fetal well being. IoT based fetal health classification leverages IoT technology to remotely assess and monitor fetal well being in real time. Continuous data streams including uterine contractions, fetal heart rate (FHR), and movement patterns can be gathered and analyzed by incorporating sensors with cloud machine learning (ML) and computing algorithms. This allows prompt diagnosis of distress indicators or abnormalities, simplifying quick measures to optimize prenatal care outcomes. Furthermore, IoT based systems provide an opportunity for personalized monitoring of individual pregnancies, enhancing fetal and maternal health monitoring through gestation. On the other hand, the present technology in medical applications could not offer an easily accessible, long term, and effective way for fetal movement monitoring. Lately, ML and deep learning (DL) approaches have been considered appropriate for the automatic classification of fetal health. This study presents an IoT assisted Fetal Health Detection and Classification using the Mother Optimization Algorithm with Deep Learning (AFHDC MOADL) method. The goal of the AFHDC MOADL technique is to accurately classify fetal health into three different classes such as normal, suspect, and pathological. In the AFHDC MOADL technique, a multi faceted process is involved. Primarily, the AFHDC MOADL technique involves IoT devices for the data acquisition process which collects fetal health related data. Besides, the AFHDC MOADL technique undergoes data pre processing in two ways such as K nearest neighbor (KNN) based data imputation and standard scaler. The AFHDC MOADL technique designs a mother optimization algorithm (MOA) to decrease the high dimensionality problem, which selects an optimal subset of features. A graph convolutional neural network (GCN) model is exploited for the fetal health classification. Finally, the root mean square propagation (RMSProp) optimizer can be utilized for optimum hyper parameter selection of the GCN technique. The simulation outcomes of the AFHDC MOADL algorithm can be assessed on the Fetal Health Classification dataset from the Kaggle dataset. The experimental validation highlighted the significant performance of the AFHDC MOADL technique over recent DL approaches.