Smart Health Analysis System Using Machine Learning and Deep Learning (Ayusense)

AyuSense is a Smart Health Analysis System designed to automate the detection and monitoring of various medical conditions using advanced machine learning and deep learning techniques. The system integrates multiple types of health data, including clinical tabular data (such as Complete Blood Count and other laboratory results), medical images (chest X-rays, CT scans, and skin lesion images), and biosignals like 12-lead ECG recordings. Machine learning algorithms, such as Random Forests, are employed for structured clinical data analysis, while deep learning architectures, including Convolutional Neural Networks (CNNs) and pretrained models like MobileNetV2 and ResNet50, are utilized for medical image classification and ECG signal analysis. The system includes preprocessing, normalization, data augmentation, and multi-class/multi-label prediction pipelines to enhance model performance and reliability. Extensive evaluation across multiple datasets demonstrates high predictive accuracy, F1-scores, and area-under-curve (AUC) metrics, validating the system’s capability to provide accurate and automated health assessments. By combining diverse medical data modalities into a unified framework, AyuSense offers a scalable and interpretable platform for comprehensive health analysis and automated diagnostic prediction.