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This study aims to apply cox regression analysis to predict the patient's survival to dengue disease occurring in Palopo. This study uses clinical data, namely the results of laboratory tests to determine the effect on the patient's healing period. Laboratory test results used are platelets and hematocrit. By using the MPLE method to obtain parameter estimation in the cox regression model, it is known that platelets have a stronger effect for patient resistance on DHF than hematocrit. This is based on the p-value obtained from the analysis less than alpha (0.05), which is equal to 0.0433. Patients who had an average platelet below normal when experiencing DHF are longer in their recovery period. In addition, patients with DHF ≤ 2 days, the probability to survive and recover was 90%.

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An otorhinolaryngologist (ORL) or general practitioner diagnoses ear disease based on ear image information. However, general practitioners refer patients to ORL for chronic ear disease because the image of ear disease has high complexity, variety, and little difference between diseases. An artificial intelligence-based approach is needed to make it easier for doctors to diagnose ear diseases based on ear image information, such as the Convolutional Neural Network (CNN). This paper describes how CNN was designed to generate CNN models used to classify ear diseases. The model was developed using an ear image dataset from the practice of an ORL at the University of Mataram Teaching Hospital. This work aims to find the best CNN model for classifying ear diseases applicable to android mobile devices. Furthermore, the best CNN model is deployed for an Android-based application integrated with the Endoscope Ear Cleaning Tool Kit for registering patient ear images. The experimental results show 83% accuracy, 86% precision, 86% recall, and 4ms inference time. The application produces a System Usability Scale of 76.88% for testing, which shows it is easy to use. This achievement shows that the model can be developed and integrated into an ENT expert system. In the future, the ENT expert system can be operated by workers in community health centres/clinics to assist leading health them in diagnosing ENT diseases early.