NAIVE BAYES AND PARTICLE SWARM OPTIMIZATION IN EARLY DETECTION OF CHRONIC KIDNEY DISEASE

Hafis Nurdin (Informatics, Faculty of Information Technology, Universitas Nusa Mandiri, Indonesia)
Suhardjono Suhardjono (Information Systems, Faculty of Informatics Engineering, Universitas Bina Sarana Informatika, Indonesia)
Anus Wuryanto (Information Systems, Faculty of Informatics Engineering, Universitas Bina Sarana Informatika, Indonesia)
Dewi Yuliandari (Information Systems, Faculty of Informatics Engineering, Universitas Bina Sarana Informatika, Indonesia)
Hari Sugiarto (Accounting Information System, Faculty of Informatics Engineering, Universitas Bina Sarana Informatika, Indonesia)

Jurnal Teknik Informatika (JUTIF)
Vol. 5 No. 3 (2024): JUTIF Volume 5, Number 3, June 2024

Chronic Kidney Disease (CKD) is a global health problem that requires early detection to reduce the risk of complications and disease progression. The Naïve Bayes (NB) algorithm has been proven effective in detecting CKD but its accuracy still varies. The problem with previous research is that it has not fully optimized existing algorithms in terms of accuracy and efficiency. This research aims to develop a more accurate and efficient early detection method for CKD using the NB algorithm and Particle Swarm Optimization (PSO). The NB method is known for its speed and ease of implementation, with global search capabilities and PSO for parameter optimization. Dataset from the UCI repository, which includes data pre-processing, NB implementation, performance evaluation, and enhancement with PSO. The results of NB+PSO show a significant increase in accuracy of 95.75% from 95.00% and Area Under Curve (AUC) value of 0.910% from 0.802% compared to the use of NB alone. The conclusion of this study is that the combination of NB+PSO increases effectiveness in early detection of CKD. This research opens up opportunities for further development in the medical field, especially in improving the diagnostic accuracy of other diseases.