Adaptive Neuro-Fuzzy and Deep Neural Network Integration for Infant Health Status Prediction Using Multi-Modal Sensor Data and Circuitry Incubator Simulation

This dataset and manuscript present a novel integration of Adaptive Neuro-Fuzzy Inference System (ANFIS) and Deep Neural Network (DNN) for infant health monitoring. The work leverages multimodal sensor data (skin temperature, humidity, oxygen saturation, heart rate, respiratory rate, body movement, and sleep patterns) and a circuitry incubator simulation to generate synthetic data for robustness.The CSV dataset (Study IDs 0–48) contains detailed incubator parameters, hypothermia events, thermal support days, and discharge outcomes. The manuscript explains methodology, feature extraction, data augmentation, and comparative benchmarking against architectures such as AlexNet, ResNet50, Darknet53, DenseNet169, EfficientNetB5, and Deep CNN.