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All Journal International Journal of Advances in Applied Sciences Bulletin of Electrical Engineering and Informatics Journal Industrial Servicess JUITA : Jurnal Informatika POSITIF CESS (Journal of Computer Engineering, System and Science) Jurnal The Messenger Sinkron : Jurnal dan Penelitian Teknik Informatika Jurnal Informatika Bianglala Informatika : Jurnal Komputer dan Informatika Akademi Bina Sarana Informatika Yogyakarta Indonesian Journal on Software Engineering (IJSE) Jurnal Manajemen STIE Muhammadiyah Palopo Bina Insani ICT Journal JURNAL MEDIA INFORMATIKA BUDIDARMA Information System for Educators and Professionals : Journal of Information System JITK (Jurnal Ilmu Pengetahuan dan Komputer) Techno Nusa Mandiri : Journal of Computing and Information Technology Edukasi Islami: Jurnal Pendidikan Islam Jurnal Hukum Ekonomi Syariah Jurnal Riset Informatika Journal of Information System, Applied, Management, Accounting and Research JURSIMA (Jurnal Sistem Informasi dan Manajemen) Al Marhalah Kordinat : Jurnal Komunikasi antar Perguruan Tinggi Agama Islam JAMI: Jurnal Ahli Muda Indonesia Al-Liqo: Jurnal Pendidikan Islam Journal of Students‘ Research in Computer Science (JSRCS) Indonesian Journal of Networking and Security - IJNS Kontribusi: Jurnal Penelitian dan Pengabdian Kepada Masyarakat AJAD : Jurnal Pengabdian kepada Masyarakat Paradigma INFORMATION SYSTEM FOR EDUCATORS AND PROFESSIONALS : Journal of Information System Journal of Training and Community Service Adpertisi JURSIMA TONGKONAN: Jurnal Pengabdian Masyarakat Jurnal Sistem Informasi dan Manajemen Jurnal Intelek Dan Cendikiawan Nusantara Ta'lim Ta'lim : Jurnal Multidisiplin Ilmu
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Journal : Paradigma

Building a Predictive Model for Chronic Kidney Disease: Integrating KNN and PSO Widodo, Slamet; Brawijaya, Herlambang; Samudi, Samudi
Paradigma - Jurnal Komputer dan Informatika Vol. 26 No. 1 (2024): March 2024 Period
Publisher : LPPM Universitas Bina Sarana Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31294/p.v26i1.3282

Abstract

This study examines the improvement of prediction accuracy for Chronic Kidney Disease (CKD) through the integration of the K-Nearest Neighbors (KNN) method with Particle Swarm Optimization (PSO). Amidst the rising prevalence of CKD, closely related to diabetes and hypertension, early detection of CKD becomes a significant challenge, especially in Indonesia where access to healthcare facilities and public awareness remain limited. This study utilizes the Chronic Kidney Disease dataset from the UCI Machine Learning repository, encompassing 400 patient records with 24 clinical, laboratory, and demographic variables. With the KNN method, this approach classifies data based on feature proximity, while PSO is used for feature selection and parameter optimization, enhancing the model's accuracy and efficiency in identifying CKD at early stages. The findings indicate a significant improvement in prediction accuracy, from 80.00% using KNN to 97.75% after integration with PSO. These results affirm that the combined approach of KNN and PSO holds great potential in improving early detection and management of CKD, paving the way for further research into practical applications in the healthcare field.
Building a Predictive Model for Chronic Kidney Disease: Integrating KNN and PSO Widodo, Slamet; Brawijaya, Herlambang; Samudi, Samudi
Paradigma - Jurnal Komputer dan Informatika Vol. 26 No. 1 (2024): March 2024 Period
Publisher : LPPM Universitas Bina Sarana Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31294/p.v26i1.3282

Abstract

This study examines the improvement of prediction accuracy for Chronic Kidney Disease (CKD) through the integration of the K-Nearest Neighbors (KNN) method with Particle Swarm Optimization (PSO). Amidst the rising prevalence of CKD, closely related to diabetes and hypertension, early detection of CKD becomes a significant challenge, especially in Indonesia where access to healthcare facilities and public awareness remain limited. This study utilizes the Chronic Kidney Disease dataset from the UCI Machine Learning repository, encompassing 400 patient records with 24 clinical, laboratory, and demographic variables. With the KNN method, this approach classifies data based on feature proximity, while PSO is used for feature selection and parameter optimization, enhancing the model's accuracy and efficiency in identifying CKD at early stages. The findings indicate a significant improvement in prediction accuracy, from 80.00% using KNN to 97.75% after integration with PSO. These results affirm that the combined approach of KNN and PSO holds great potential in improving early detection and management of CKD, paving the way for further research into practical applications in the healthcare field.