Implementation of Extreme Learning Machine Method with Particle Swarm Optimization to Classify of Chronic Kidney Disease

Muhammad Mursyidan Amini; Muhammad Itqan  Mazdadi; Muliadi Muliadi; Mohammad Reza  Faisal; Triando Hamonangan  Saragih

doi:10.35882/jeeemi.v6i4.561

Muhammad Mursyidan Amini Lambung Mangkurat University
Muhammad Itqan Mazdadi Computer Science Department, Lambung Mangkurat University, Banjarbaru, South Kalimantan, Indonesia https://orcid.org/0000-0002-8710-4616
Muliadi Muliadi Computer Science Department, Lambung Mangkurat University, Banjarbaru, South Kalimantan, Indonesia https://orcid.org/0000-0003-2871-9482
Mohammad Reza Faisal Computer Science Department, Lambung Mangkurat University, Banjarbaru, South Kalimantan, Indonesia https://orcid.org/0000-0001-5748-7639
Triando Hamonangan Saragih Computer Science Department, Lambung Mangkurat University, Banjarbaru, South Kalimantan, Indonesia https://orcid.org/0000-0003-4346-3323

DOI: https://doi.org/10.35882/jeeemi.v6i4.561

Keywords: Chronic Kidney Disease, Extreme Learning Machine, Particle Swarm Optimization

Abstract

Kidney Disease (CKD) appears as a pathological condition due to infection of the kidneys and blockages due to the formation of kidney stones. In the Indonesian context, kidney disease is the second most common disease after heart disease based on BPJS Health data. Notably, in this scenario, medical practitioners and individuals with specialized knowledge in the field are still faced with challenges in effectively classifying CKD cases, thereby making them vulnerable to erroneous diagnostic conclusions. The main objective underlying this particular research effort revolves around increasing the level of accuracy that characterizes the CKD classification process by orchestrating the incorporation of Particle Swarm Optimization (PSO) techniques into the operational framework of Extreme Learning Machines (ELM) with the aim of ensuring optimal results. Configuration of input weights and critical biases to achieve superior diagnostic results. The results obtained from the investigation process include many numerical parameters including but not limited to determining the ideal number of hidden nodes set at 11, population size 80, identification of the most preferred number of iterations denoted by the Best value of 20, aggregate inertia weight assessed at 0.5, along with the constants 1 (c1) and 2 (c2) each registering a value of 1, culminating in the achievement of an accuracy metric pegged at an impressive level of 98.50%. Consequently, the implications obtained from this empirical investigation strengthen the assertion that the use of PSO optimization strategies within the operational framework of ELM has the potential to yield major advances in the classification evaluation domain related to CKD diagnosis.

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