Cloud-Enabled AI Framework for Real-Time AI-Driven Dual-Target Decision Support System in Emergency Medical Services

Abstract

The rapid increase in hypertension and diabetes cases has created a strong demand for advanced healthcare systems that support early diagnosis and effective clinical decision-making. Managing these chronic conditions requires continuous evaluation of multiple patient attributes, which becomes difficult when performed manually. Traditional approaches rely heavily on basic statistical techniques and human interpretation, making them inefficient for handling large-scale data, identifying complex patterns, and providing real-time predictions. As a result, such systems often lack accuracy, scalability, and reliability, particularly in distributed healthcare environments. A key challenge lies in their inability to handle imbalanced datasets, perform multi-condition prediction, and enable seamless remote communication between systems. To address these limitations, this work proposes a real-time decision support system powered by Artificial Intelligence (AI) using a dual client–server architecture. The server handles data preprocessing, model training, and prediction using Machine Learning (ML) algorithms such as Complement Naive Bayes (CNB), Multinomial Naive Bayes (MNB), Perceptron, and a Tao Tree Classifier (TTC). Preprocessing methods include Label Encoding and K-Means Synthetic Minority Oversampling Technique (KMeans-SMOTE) to manage categorical data and class imbalance. A Flask-based Application Programming Interface (API) using Hypertext Transfer Protocol (HTTP) enables efficient communication between the client and server. The client system allows users to upload datasets, which are processed remotely to predict blood pressure categories and diabetes status. Lightning Memory-Mapped Database (LMDB) is used for secure and efficient data management. The proposed system ensures accurate multi-target prediction, real-time accessibility, and seamless device communication, ultimately improving healthcare services, reducing manual effort, and supporting better clinical decisions.