Journal of Lupus Disease Classification Study Using Naïve Bayes Method

Teti  Desyani; Bagas Mahendra  Putra; Al  Haura; La  Juanda; Vivi  Ainun; Perani Rosyani

doi:10.55927/fjst.v4i1.13489

Authors

Teti Desyani Faculty of Computer Science, Informatics Engineering, Pamulang University
Bagas Mahendra Putra Faculty of Computer Science, Informatics Engineering, Pamulang University
Al Haura Faculty of Computer Science, Informatics Engineering, Pamulang University
La Juanda Faculty of Computer Science, Informatics Engineering, Pamulang University
Vivi Ainun Faculty of Computer Science, Informatics Engineering, Pamulang University
Perani Rosyani Faculty of Computer Science, Informatics Engineering, Pamulang University

DOI:

https://doi.org/10.55927/fjst.v4i1.13489

Keywords:

Autoimmune, Lupus, Naïve Bayes

Abstract

The chronic autoimmune illness known as systemic lupus erythematosus (SLE) is typified by tissue destruction in multiple organs and systemic inflammation. Diagnosing this condition might be difficult because of its varied and fluctuating clinical symptoms. The goal of this research is to use clinical and laboratory data to create a classification model for SLE diagnosis using the Naïve Bayes approach. Age, gender, clinical symptoms, and the outcomes of laboratory tests are among the information gathered for this study. This approach is crucial for helping with SLE management and early diagnosis. The Naïve Bayes model was used to assess and categorize these data according to the severity of the condition. The accuracy, precision, and recall measures were used in the study to evaluate the Naïve Bayes model. The outcomes demonstrated how well the Naïve Bayes algorithm can categorize SLE patients.

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