NeoCoD: A New Standard in IoT-Based Predictive Analytics for Neonatal Health Monitoring

Introduction: The importance of neonatal health monitoring lies in early detection, and thus, addressing NICU (Neonatal Incentive Care Unit) patients with timely medical treatment. Current predictive models typically use only either medical imaging or sensor data, limiting their efficiency for real time detection of multiple neonatal conditions. To overcome this limitation, we introduce NeoCoD (Neonatal Cause of Disease Predictor), an IoT-based predictive analytics system that combines physiological markers based on sensor data with medical imaging data to better assess the health of newborns. Objectives: The main purpose of this study was to design a hybrid deep learning model using ResNet50 (Residual Neural Network) for medical images feature extraction and Long Short-Term Memory (LSTM) for the temporal analysis of IoT based sensor data. The dataset contains 7132 samples of 270 neonates collected from Sparsh Medical Hospital, Ahmedabad, Dr. Babasaheb Ambedkar Hospital, Mumbai, and publicly available neonatal health data. We colligate neonatal diseases (e.g., Neonatal Respiratory Distress Syndrome (NRDS), Neonatal Sepsis, Jaundice, Hypothermia, Hypoglycemia, Neonatal Pneumonia, Neonatal Apnea, Perinatal Asphyxia, Neonatal Meningitis, Neonatal Encephalopathy, Hyperbilirubinemia, Low Birth Weight Complications) for our study. Methods: The NeoCoD model pipeline consists of the following multi stages: data preprocessing, feature engineering, model training and performance evaluation. Feature scaling: the feature scaling was performed and rescaled the features to preprocess the information. We trained the model with categorical cross-entropy loss, Adam optimizer and regularization methods like dropout (0.3) layer and batch normalization. For comparing the performance of NeoCoD with ne trivial solutions, Random Forest and SVM models, accuracy, precision, recall, and F1-score are used. Results: Experimental results show that NeoCoD considerably outperforms traditional models with accuracy of 92.5% against Random Forest (87.3%) and SVM (84.5%). This model is capable of rapid multimodal processing and prediction on incoming data, providing real-time predictions with 2 seconds response time which makes it particularly suitable for NICU applications. Conclusions: These results establish NeoCoD as a high-accuracy, scalable neonatal health monitoring system for infant dieses prediction at earlier stage. Going forward, work will center on increasing generalisability across different hospital contexts, increasing computational efficiency, and improving interpretability for clinical decision-making.