Penerapan Naive Bayes Untuk Klasifikasi Penyakit Endokrin Pada Pasien Lansia

Abstract

The application of the Naïve Bayes algorithm has shown great potential in the classification of endocrine diseases in elderly patients. This study aims to develop a classification model using the algorithm, utilizing diabetes-related data obtained from public datasets. The process involves data collection, preprocessing, model training, and evaluation using Orange software. The results show that Naïve Bayes' algorithm is able to achieve high accuracy in data classification. The implementation of this model is expected to be a medical decision support system for faster and more accurate diagnosis, as well as improve the efficiency of health services for elderly patients. The advantages of this method lie in its ease of use, time efficiency, and intuitive visualization capabilities, making it an effective tool in medical data analysis. The main advantages of this approach include ease of implementation, time efficiency in data analysis, and the ability to visualize intuitively through a software interface. Thus, this research not only contributes to the development of health technology but also opens up opportunities for further integration with more complex AI-based health information systems. The adoption of this model is expected to be able to encourage the improvement of the quality of health services in the future.