EXAMINATION OF UNREMITTING KIDNEY ILLNESS BY UTILIZING MACHINE LEARNING CLASSIFIERS Fareeha Sarwar 1, 2 , Nuno Garrido 1 , Pedro Sebastião 1 and Akmal Rehan 2 1 Instituto de Telecomunicações (IT-IUL) Ed. Armadas, 1649-026, Lisbon, Portugal 2 Faculty of Science, Department of Computer Science University of Agriculture, Faisalabad, Pakistan ABSTRACT Chronic kidney disease is a rising health issue that affects millions of people worldwide. Early detection and characterization of this disease is essential for effective management and control. This disease is associated with several serious health risks, such as cardiovascular disease, increased risk of stroke, and end-stage renal disease, which can be effectively prevented by early detection and treatment. Medical scientists rely on machine learning algorithms to diagnose the disease accurately at its outset. Recently, adding value to healthcare is being accomplished through the integration of machine learning algorithms into mobile health solution. Considering this, this paper proposes a predictive model of three machine learning classifiers, including Support Vector Machine, Decision Tree, and Multilayer Perceptron for chronic kidney disease prediction. The performance of the model was assessed using confusion matrix and executed in popular machine learning software tools such as WEKA and Rapid Minor. The study found that support vector machine yielded the highest accuracy rate of 98% in predicting chronic kidney disease in WEKA among other standard classifiers by using 10-fold cross validation. In addition, the proposed prediction model has been compared with existing models in terms of accuracy, sensitivity, and specificity. The experimental results indicate that the proposed predictive model shows promising results. These findings could integrate with the development of mobile health solution and other innovative approaches to prevent and treat this debilitating condition. KEYWORDS Machine Learning Classifiers, Chronic Kidney Disease, WEKA, Rapid Minor, Mobile Health Solution 1. INTRODUCTION Worldwide, non-communicable diseases (NCDs) have replaced communicable diseases as the leading cause of morbidity and premature death. In low- and middle-income countries, 80% of the burden occurs, and 25% occurs in people under 60. NCDs are expected to have a significant impact on the global economy: by 2015, just two diseases (cardiovascular disease and diabetes) will account for 5% of global GDP. Heart disease, stroke, and peripheral vascular disease account for approximately half of the economic burden, causing more deaths than HIV/AIDS, malaria, and tuberculosis combined. Among diabetes and cardiovascular disease (including hypertension), kidney disease is an essential determinant of poor health outcomes, and the World Health Organization recommends that national NCD programs focus on preventing kidney disease, particularly at the primary care level (Couser, Remuzzi, Mendis, & Tonelli, 2011). As chronic kidney disease (CKD) does not follow any age limit, it can appear at any age. Further, if one has already developed CKD, one is more likely to experience sudden deterioration of kidney function. Procrastination can cause severe kidney damage. This disease requires early detection in order to be treated successfully. However, CKD doesn't show symptoms in the initial stages, making it impossible to identify it without testing. A person with milder chronic kidney disease may do not show any disease-related symptoms, and that can make it hard to predict. The effected person will show symptoms that are very common, like nausea, high blood pressure and blood in your urine. At this stage if CKD is detected, it can be cured and monitored to prevent prolonged damage to the kidney. Urine and blood test can be conducted to detect it an early stage. When chronic kidney disease worsens or at a high stage, it starts showing symptoms like anemia, low immune response, blood pressure, nerve damage, and weak bones. But it is also observed that some people with chronic kidney International Conferences ICT, Society, and Human Beings 2023; and e-Health 2023; Connected Smart Cities 2023; and Big Data Analytics, Data Mining and Computational Intelligence 2023 191