Explainable Prediction of Chronic Renal Disease in the Colombian Population Using Neural Networks and Case-Based Reasoning

This paper presents a neural network-based classifier to predict whether a person is at risk of developing chronic kidney disease (CKD). The model is trained with the demographic data and medical care information of two population groups: on the one hand, people diagnosed with CKD in Colombia during 2018, and on the other, a sample of people without a diagnosis of this disease. Once the model is trained and evaluation metrics for classification algorithms are applied, the model achieves 95% accuracy in the test data set, making its application for disease prognosis feasible. However, despite the demonstrated efficiency of the neural networks to predict CKD, this machine-learning paradigm is opaque to the expert regarding the explanation of the outcome. Current research on eXplainable AI proposes the use of twin systems, where a black-box machine-learning method is complemented by another white-box method that provides explanations about the predicted values. Case-Based Reasoning (CBR) has proved to be an ideal complement as this paradigm is able to find explanatory cases for an explanation-by-example justification of a neural network’s prediction. In this paper, we apply and validate a NN-CBR twin system for the explanation of CKD predictions. As a result of this research, 3,494,516 people were identified as being at risk of developing CKD in Colombia, or 7% of the total population.

[1]  Guillermo Jiménez-Díaz,et al.  An Algorithm Independent Case-Based Explanation Approach for Recommender Systems Using Interaction Graphs , 2019, ICCBR.

[2]  H. Benali,et al.  Support vector machine-based classification of Alzheimer’s disease from whole-brain anatomical MRI , 2009, Neuroradiology.

[3]  Xiaohui Liang,et al.  A hybrid neural network model for predicting kidney disease in hypertension patients based on electronic health records , 2019, BMC Medical Informatics and Decision Making.

[4]  Soumya Sen,et al.  Cuckoo search coupled artificial neural network in detection of chronic kidney disease , 2017, 2017 1st International Conference on Electronics, Materials Engineering and Nano-Technology (IEMENTech).

[5]  Nitish Srivastava,et al.  Improving Neural Networks with Dropout , 2013 .

[6]  Enric Plaza,et al.  Noticeably New: Case Reuse in Originality-Driven Tasks , 2008, ECCBR.

[7]  S. Roy,et al.  Prediction of Chronic Kidney Diseases Using Deep Artificial Neural Network Technique , 2019, Computer Aided Intervention and Diagnostics in Clinical and Medical Images.

[8]  Yu-Bin Yang,et al.  Lung cancer cell identification based on artificial neural network ensembles , 2002, Artif. Intell. Medicine.

[9]  Nilanjan Dey,et al.  Detection of Chronic Kidney Disease: A NN-GA-Based Approach , 2018 .

[10]  Kurt Hornik,et al.  Multilayer feedforward networks are universal approximators , 1989, Neural Networks.

[11]  Ying Ju,et al.  Predicting Diabetes Mellitus With Machine Learning Techniques , 2018, Front. Genet..

[12]  Muin J. Khoury,et al.  Application of support vector machine modeling for prediction of common diseases: the case of diabetes and pre-diabetes , 2010, BMC Medical Informatics Decis. Mak..

[13]  Abdullah Al Imran,et al.  Classification of Chronic Kidney Disease using Logistic Regression, Feedforward Neural Network and Wide & Deep Learning , 2018, 2018 International Conference on Innovation in Engineering and Technology (ICIET).

[14]  Andrew P. Bradley,et al.  The use of the area under the ROC curve in the evaluation of machine learning algorithms , 1997, Pattern Recognit..

[15]  Christopher M. Bishop,et al.  Regularization and complexity control in feed-forward networks , 1995 .

[16]  Corinna Cortes,et al.  Support-Vector Networks , 1995, Machine Learning.

[17]  Mahdieh Poostchi,et al.  Image analysis and machine learning for detecting malaria , 2018, Translational research : the journal of laboratory and clinical medicine.

[18]  Homay Danaei Mehr,et al.  Diagnosis of Chronic Kidney Disease Based on Support Vector Machine by Feature Selection Methods , 2017, Journal of Medical Systems.

[19]  Tom Fawcett,et al.  An introduction to ROC analysis , 2006, Pattern Recognit. Lett..

[20]  Geoffrey E. Hinton,et al.  Learning representations by back-propagating errors , 1986, Nature.

[21]  U. Raghavendra,et al.  A deep learning approach for Parkinson’s disease diagnosis from EEG signals , 2018, Neural Computing and Applications.

[22]  Trevor Hastie,et al.  An Introduction to Statistical Learning , 2013, Springer Texts in Statistics.

[23]  W. Pitts,et al.  A Logical Calculus of the Ideas Immanent in Nervous Activity (1943) , 2021, Ideas That Created the Future.

[24]  A. Subasi,et al.  Diagnosis of Chronic Kidney Disease by Using Random Forest , 2017 .

[25]  David B. Leake,et al.  CBR Confidence as a Basis for Confidence in Black Box Systems , 2019, ICCBR.

[26]  Jimmy Ba,et al.  Adam: A Method for Stochastic Optimization , 2014, ICLR.

[27]  Leo Breiman,et al.  Random Forests , 2001, Machine Learning.

[28]  El Houssainy A. Rady,et al.  Prediction of kidney disease stages using data mining algorithms , 2019, Informatics in Medicine Unlocked.

[29]  Arthur L. Samuel,et al.  Some Studies in Machine Learning Using the Game of Checkers , 1967, IBM J. Res. Dev..

[30]  Charu C. Aggarwal,et al.  Neural Networks and Deep Learning , 2018, Springer International Publishing.

[31]  Seyed Abolghasem Mirroshandel,et al.  A novel method for predicting kidney stone type using ensemble learning , 2017, Artif. Intell. Medicine.

[32]  Xiao-Fei Liu,et al.  An MLP Classifier for Prediction of HBV-Induced Liver Cirrhosis Using Routinely Available Clinical Parameters , 2013, Disease markers.

[33]  Yee Whye Teh,et al.  A Fast Learning Algorithm for Deep Belief Nets , 2006, Neural Computation.

[34]  Shrinivas D. Desai,et al.  Intelligent Heart Disease Prediction System Using Probabilistic Neural Network , 2013 .

[35]  David Leake,et al.  Case-Based Reasoning: Experiences, Lessons and Future Directions , 1996 .

[36]  Konstantin Tretyakov,et al.  Machine Learning Techniques in Spam Filtering , 2004 .

[37]  Abhishek,et al.  Artificial Neural Networks for Diagnosis of Kidney Stones Disease , 2012 .

[38]  Andrew H. Beck,et al.  Diagnostic Assessment of Deep Learning Algorithms for Detection of Lymph Node Metastases in Women With Breast Cancer , 2017, JAMA.

[39]  Parul Parashar,et al.  Neural Networks in Machine Learning , 2014 .

[40]  Agnar Aamodt,et al.  Case-Based Reasoning: Foundational Issues, Methodological Variations, and System Approaches , 1994, AI Commun..

[41]  Sibo Zhu,et al.  Comparison and development of machine learning tools in the prediction of chronic kidney disease progression , 2019, Journal of Translational Medicine.

[42]  Yann LeCun,et al.  What is the best multi-stage architecture for object recognition? , 2009, 2009 IEEE 12th International Conference on Computer Vision.

[43]  Vivian West,et al.  Improving diagnostic accuracy using a hierarchical neural network to model decision subtasks , 2000, Int. J. Medical Informatics.

[44]  Leo Breiman,et al.  Bagging Predictors , 1996, Machine Learning.

[45]  Mark T. Keane,et al.  How Case-Based Reasoning Explains Neural Networks: A Theoretical Analysis of XAI Using Post-Hoc Explanation-by-Example from a Survey of ANN-CBR Twin-Systems , 2019, ICCBR.

[46]  Chuan Yi Tang,et al.  Chronic Kidney Disease Survival Prediction with Artificial Neural Networks , 2018, 2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM).

[47]  P. Lakhani,et al.  Deep Learning at Chest Radiography: Automated Classification of Pulmonary Tuberculosis by Using Convolutional Neural Networks. , 2017, Radiology.

[48]  V. Vapnik.  Estimation of Dependences Based on Empirical Data , 2006 .