# Explainable Machine Learning Model for Chronic Kidney Disease Prediction
## 1 Introduction  
The progressive decline in kidney function over time characterizes CKD, which represents a significant and escalating public health concern globally. The kidneys must filter out excess water and waste from the bloodstream to produce urine and keep the body at a steady temperature and pressure. The term \"chronic kidney disease\" is derived from the fact that the kidneys are gradually damaged, and the condition is frequently undetected until it is too late. This quiet progression of CKD emphasizes the importance of early detection because the body can adjust to lower kidney function in the early stages, which is why there are typically no symptoms. Many people don't get treatment until their disease has gotten much worse, and the diagnosis is often made by chance in a blood or urine test that would have been normal otherwise [1; 2; 3; 4]. Risk factors like hypertension, diabetes, and cardiovascular diseases significantly increase the likelihood of developing CKD [5; 6]. The disease's prevalence has increased globally, partly due to the rise in these risk factors, with an estimated 843.6 million individuals affected in 2017 [7]. Despite the progress made in the treatment of CKD, it remains a significant cause of mortality on a global scale, requiring vigilant monitoring and intervention to reduce its progression and impact [8]. Healthcare practitioners depend on essential diagnostic procedures, such as the glomerular filtration rate (GFR) and urine tests for albumin, to diagnose and monitor CKD [9]. Using the blood test results and patient information, the GFR indicates how well the kidneys function to rid the body [10]. A GFR of less than 60 may signal kidney disease, whereas numbers under 15 mean kidney failure and the need for dialysis or transplant [11]. A urine test for albumin is a valuable way to identify kidney disease because a properly functioning kidney does not let this protein into your pee [12]. While undoubtedly helpful, these tests have their limitations in the accuracy of predicting disease progression or revealing its underlying physiological mechanisms [13; 14]. ML has demonstrated significant potential in improving the diagnosis of various medical conditions [15; 16; 17]. In particular, the analysis and diagnosis of CKD have benefited from the application of ML techniques [18; 19; 20]. ML models can sift through complex datasets in search of insights and patterns that would be impossible to uncover with more conventional forms of research [21; 22]. However, many ML models are notoriously difficult to understand, leading some to call them \"black boxes.\" A black box model provides outputs without revealing or explaining the internal decision-making processes [23]. While these models excel at prediction and classification, their lack of interpretability and transparency can hinder therapeutic decision-making, leaving patients and doctors with unanswered questions about the diagnosis and treatment [24; 25]. Introducing an explainable ML model is a significant advancement in this area. By integrating a MLP model with LIME, our suggested approach does more than just predict CKD; it also explains how and why it achieved this prediction. This openness allows for a better knowledge of the disease's origins, allowing for more informed and personalized clinical recommendations. The proposed explainable ML model is being thought about as a way to get around the main problems with current and unclear ML methods for diagnosing CKD. An essential resource for nephrologists, the model delves deep into the variables that influence its projections. This innovation makes CKD detection more precise and sheds light on how the disease develops. As a result, patients may get more effective, personalized care. Here is the structure of the rest of the paper: Section 2 presents a comprehensive literature review on CKD from the last two years, highlighting the unique features of our study while also addressing their shortcomings. Our methodology, outlining the proposed system model, is described in Section 3.