Real-Time Heart Failure Prediction: An Approach for Ambient Assisted Living - Almeida, Cristiano Paschoalim de; Bernardino, Heder Soares; Souza, Jairo Francisco de; Campos, Luciana Conceição Dias (Anais do Simpósio Brasileiro de Sistemas de Informação (SBSI); 2025: Anais do XXI Simpósio Brasileiro de Sistemas de Informação, 2025)
Context: The integration of IoT in healthcare has enhanced the capability of Ambient Assisted Living by enabling reliable real-time health monitoring, particularly for heart failure identification. Predictive models play a crucial role in identifying potential heart failures, improving patient outcomes through continuous monitoring and analysis. Problem: Traditional predictive models rely on centralized servers, facing issues like network latency, disruptions, and data overload. These challenges hinder real-time health data processing, limiting their ability to provide timely heart failure risk predictions. Solution: This study proposes a framework that embeds machine learning models directly into mobile devices, leveraging edge computing for real-time heart failure risk assessment. By processing data locally, the solution reduces latency, enhances reliability, and ensures greater data privacy while maintaining predictive accuracy.IS Theory: The framework aligns with socio-technical IS theory by integrating technical innovations and user-centric needs. Embedding predictive models into mobile devices enhances real-time predictions and patient care, bridging technology and human interaction. Method: A heart failure prediction dataset was utilized, employing supervised classification algorithms—Random Forest, K-Nearest Neighbors (KNN), and Logistic Regression. Data preprocessing included handling missing values, feature scaling, and encoding. The trained models were deployed on mobile devices using ONNX Runtime for efficient real-time inference. Summary of Results: The proposed system successfully performed real-time heart failure risk prediction on mobile devices, achieving competitive accuracy. Random Forest outperformed other models, reaching an accuracy of 85.33%, demonstrating its effectiveness in edge computing environments. The approach significantly mitigates the connectivity and latency challenges of centralized systems while enhancing data security. Impact in the IS Area: This research highlights the potential of edge computing to enhance real-time healthcare applications by reducing reliance on cloud infrastructure. Future works includes conducting real-environment patient evaluations to validate the system’s clinical applicability, integrating biomedical sensors, improving predictive models using deep learning, and exploring federated learning for privacy-preserving model training.