Fig. 1: Flowchart of research.

a Preprocessing and discretization of continuous variables in 2 multi-modal datasets: NIH Chest X-ray Dataset published by the National Institute of Health Clinical Center. It comprises 112,120 chest X-ray images of 30,805 unique patients. Each radiographic image is labeled with common thorax diseases of one or more of 14 types: atelectasis, cardiomegaly, consolidation, edema, effusion, emphysema, fibrosis, hernia, infiltration, mass, nodule, pleural thickening, pneumonia, and pneumothorax. Custom-built Cervical cancer dataset contains features related to cervical cancer risk factors, where each row represents a data sample of a subject, and each column represents a feature. The features include age, sexual behavior, lifestyle habits, medical history, and more. There is are total of 858 samples in this dataset, with each sample having 36 features and a result indicating whether the individual has cervical cancer. The dataset consists of both continuous and discrete values. b Multi-stage risk classification pipeline based on filtering and a multivariate parallel deep learning model, MedFusionNet. Stage 1: initial risk classification. Stage 2: multivariate combination for risk classification. c Decision-making support for different risk levels.