Computer-Aided Diagnosis of Chronic Kidney Disease in Developing Countries: A Comparative Analysis of Machine Learning Techniques

Abstract

The high incidence and prevalence of chronic kidney disease (CKD), often caused by late diagnoses, is a critical public health problem, especially in developing countries such as Brazil. CKD treatment therapies, such as dialysis and kidney transplantation, increase the morbidity and mortality rates, besides the public health costs. This study analyses the usage of machine learning techniques to assist in the early diagnosis of CKD in developing countries. Qualitative and quantitative comparative analyses are, respectively, conducted using a systematic literature review and an experiment with machine learning techniques, with the k-fold cross-validation method based on the Weka software and a CKD dataset. These analyses enable a discussion on the suitability of machine learning techniques for screening for CKD risk, focusing on low-income and hard-to-reach settings of developing countries, due to the specific problems faced by them, e.g., inadequate primary health care. The study results show that the J48 decision tree is a suitable machine learning technique for such screening in developing countries, due to the easy interpretation of its classification results, with 95.00% accuracy, reaching a nearly perfect agreement with an experienced nephrologist's opinion. Conversely, random forest, naive Bayes, support vector machine, multilayer perceptron, and k-nearest neighbor techniques, respectively, yield 93.33%, 88.33%, 76.66%, 75.00%, and 71.67% accuracy, presenting at least moderate agreement with the nephrologist, at the cost of a more difficult interpretation of the classification results. INDEX TERMS Reviews, machine learning, medical diagnosis.

Ensemble Feature Ranking for Cost-Based Non-Overlapping Groups: A Case Study of Chronic Kidney Disease Diagnosis in Developing Countries

“…In this regard, the selected problem is of high interest as it blights the developing countries over a long period. As machine learning-based screening and diagnosis applications provide a workable solution to the many problems faced by the healthcare sector, therefore, recently a lot of scholarly work is reported on developing disease diagnosis and management systems for the CKD [9], [10], [12], [13], [21], [22]. The current study is in the continuation of the scholarly work performed on the CKD diagnosis problem.…”

“…In this regard, a number of machine learning-based techniques are proposed to assist in CKD-related diagnosis [9]- [13]. The overall objective of the CKD diagnosis system is to accurately and reliably diagnose patients through automated clinical decision support systems.…”

“…A number of studies have reported promising results on the application of feature selection techniques to the CKD diagnosis problem. In these techniques both filter and wrapper based methods are used for selecting a set of salient features [9]- [13], [21]. In this case, it is demonstrated that a small set of highly predictive features can yield accurate and generalizable classification models.…”

Cost-sensitive Ensemble Feature Ranking and Automatic Threshold Selection for Chronic Kidney Disease Diagnosis

“…Likewise, around 2.5-11.2% of the adult population in Europe also suffer from it, while around 59% of all American adult population is at a high risk of developing kidney disease at some point [4][5]. The high incidence and prevalence of CKD are attributed to its late diagnosis, especially in developing countries [6].…”

“…In the domain of medical data mining, several intelligent clinical decision support systems are designed which tend to automate the diagnosis process [6,42]. These decision systems employ machine learning techniques that assist physicians in the diagnosis and treatment of CKD in an efficient manner [6,7,8]. Based on a number of important indicators such as blood pressure, albumin levels, blood and urea tests, potassium, and other comorbidities e.g.…”