## 1 Introduction  
Chronic kidney disease (CKD) is a serious public health problem and increases the global burden of cardiovascular morbidity and mortality [1]. According to a recent report on disease surveillance in the United States, an estimated 37 million people (15 percent of the adult population) are affected by kidney disease, with approximately 90 percent of those affected unaware of their condition [2]. In Taiwan, CKD affects an estimated 2 million people, of whom only 3.5 percent are aware of their condition. Taiwan is controversial for having the highest per capita rate of end-stage renal disease in the world, resulting in extensive dialysis treatments [3]. It is hard to say if the vastly different rate of CKD in Taiwan compared to the U.S. reflects an actual difference or the ease of access to medical care and diagnosis in Taiwan's healthcare system.  
Kidney disease is asymptomatic and often goes undiagnosed until it reaches an advanced stage [4]. Organ transplantation remains the only survival option for patients with severe end-stage organ failure. In 2018, approximately 141,000 kidney transplants were performed worldwide. Therefore, both prediction and prevention of CKD are important, not only to prevent end-stage renal failure, but also to reduce the associated risk of cardiovascular disease and death.  
Metabolic indices are increasingly used in clinical practice to predict disease, particularly in situations, such as large scale epidemiological studies, where the conventional methods of diagnosis are not feasible [5]. Recently, a number of novel metabolic indices (NMI) have been developed for disease assessment. For example, the triglyceride glucose (TyG) index, triglyceride high-density lipoprotein cholesterol (TG/HDL-C) ratio, visceral adiposity index (VAI), and the lipid accumulation product (LAP) are metabolic indices based on anthropometric and biochemical data collected in clinical practice [6].  
Engineering techniques can be used to create predictive control systems for assessing and preventing CKD. Machine leaning (ML) is one of the data mining tools applied to analyzing databases as a way to produce new knowledge. With machine learning algorithms, it is possible to evaluate lab records and other information to identify patterns that can be used for early diagnosis of CKD. Through knowledge discovery in databases, low-level data can be synthesized into high-level knowledge that may assist practitioners in gaining a better understanding of how CKD develops in order to diagnose it earlier [7]. ML-based algorithms have a wide range of applications in the medical field, including CKD risk prediction. However, to the best of our knowledge, no attempts have been made to compare the effectiveness of ML-based CKD risk prediction with the novel metabolic indices or to combine the two approaches.  
Common conventional methods of diagnosing CKD, such as albuminuria, eGFR and medical imaging, are tedious, unreliable, expensive, and can only diagnose CKD once it occurs. A cheap, accurate, and reliable tool to predict CKD risk earlier would be beneficial to save lives and avert economic losses. Identifying which novel indices better predict onset of CKD and utilizing those in machine learning could provide additional tools to support prevention strategies that can avoid the higher costs of dialysis. The goal of the study was to identify the novel metabolic indices with high predictive power and can also be combine with the conventional risk factors to develop a machine learning model that can identify individuals at risk of developing chronic kidney disease earlier and precisely at low cost with or without the conventional methods.