KLASIFIKASI PENYAKIT DIABETES MELLITUS (DM) MENGGUNAKAN ALGORITMA NAÏVE BAYES PADA KLINIK PRATAMA JAMBI - simorangkir, lucy; Putra, Akenda (JURNAL AKADEMIKA, 2024)
Diabetes or Diabetes Mellitus (DM) is a chronic metabolic disease characterized by high blood sugar levels over a long period of time. This condition occurs because the body cannot use blood sugar optimally. Diabetes can cause damage to the heart, blood vessels, eyes, kidneys, and nerves. The Naïve Bayes algorithm is one of the effective and popular classification methods in machine learning. Its advantages are simplicity and ease of implementation, even with small datasets. The application of this algorithm aims to classify diabetes mellitus and can help early detection of the disease based on the symptoms that appear. Consultation services for diabetes at the Klinik Pratama dan Apotek U.K. require several blood sugar tests, such as fasting blood sugar tests, blood sugar tests performed 2 hours after eating, and HbA1c tests. This diagnostic process takes quite a long time. Therefore, the Naïve Bayes method is used as a solution to generate predictions whether the patient is diagnosed positive or negative for diabetes. The application is built using Python programming which makes it easy to help process data. The output of this program model can be a recommendation in making an early diagnosis of diabetes mellitus. The expected result of this application is that the diagnostic process can be carried out more quickly and efficiently, thus providing significant benefits in diabetes-related health services.