Human health issues require estimation of heart rhythm anomalies, brain wave pattern abnormalities, blood parameter outliers, social media analysis, and more. Researchers propose many deep learning models to estimate these issues, and each uses neural optimizations to classify input parameters into disease types. But most of the existing models are either highly complex, or are not capable of capturing multiple body parameters, in order to perform comprehensive disease diagnosis. To overcome the challenges of feature representation and classification, this text proposes design of a high-density feature representation model for multidomain analysis of human health issues. The proposed model collects patient-specific datasets from multidomain sources like blood reports, electrocardiograms (ECGs), electroencephalograms (EEGs), social media posts, and physiological parameters to generate comprehensive health feature vectors. Fourier, Wavelet, Convolutional, and Gabor components transform vectors into multimodal feature sets. Representing the input vector into these components improves feature density, which helps identify multiple disease types. Bee Colony Optimization Models estimate high-variance feature sets using these features. A 1D CNN Model classifies these sets to help identify disease classes. The model performed well in accuracy, precision, recall, and AUC (area under the curve) tests for heart, brain, and psychological issues, making it suitable for clinical use. Compared to standard disease classification models, it improved accuracy, precision, recall, and fMeasure by 3.2%, 3.5%, 2.9%, and 2.5%, making it useful for real-time deployments. [ABSTRACT FROM AUTHOR]