Application of Neural Systems for Healthcare Decision Support: A Case Study of South Sudan
Keywords:
Artificial neural networks, Disease classification, Healthcare decision support, Low-resource settings, Machine learningAbstract
This study examines the application of neural systems for healthcare decision support in South Sudan, a country experiencing significant healthcare delivery challenges due to limited infrastructure, a shortage of qualified medical personnel, and fragmented health information systems. These constraints have made accurate disease diagnosis and effective decision-making difficult, particularly in managing widespread communicable diseases such as malaria and cholera. To address these challenges, a supervised artificial neural network (ANN) model was developed to assist in disease prediction and healthcare decision processes. The model was trained using a combination of simulated data and secondary health datasets that reflect the regional disease burden and healthcare patterns in South Sudan. By incorporating realistic health indicators and disease prevalence data, the system was designed to mimic real-world diagnostic scenarios in low-resource environments. The performance of the ANN model was evaluated using standard machine learning metrics, including accuracy, precision, recall, and F1-score, ensuring a comprehensive assessment of its predictive capabilities. The results demonstrate that the neural system achieved an overall accuracy of 87%, indicating a strong ability to predict and classify disease conditions effectively. This level of performance highlights the potential of artificial intelligence to enhance diagnostic accuracy and support healthcare professionals in resource-constrained settings. Furthermore, the findings suggest that neural systems can play a crucial role in improving healthcare outcomes by enabling faster data-driven decisions. Despite the promising results, the study also acknowledges key limitations, including data scarcity, limited digital infrastructure, and implementation challenges. Future research should focus on improving data availability, strengthening ICT systems, and integrating AI-based tools into national healthcare frameworks to maximize their long-term impact.
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