Identification of risk factors for patients with diabetes: diabetic polyneuropathy case study

Abstract

Background: Methods of data mining and analytics can be efficiently applied in medicine to develop models that use patient-specific data to predict the development of diabetic polyneuropathy. However, there is room for improvement in the accuracy of predictive models. Existing studies of diabetes polyneuropathy considered a limited number of predictors in one study to enable a comparison of efficiency of different machine learning methods with different predictors to find the most efficient one. The purpose of this study is the implementation of machine learning methods for identifying the risk of diabetes polyneuropathy based on structured electronic medical records collected in databases of medical information systems. Methods: For the purposes of our study, we developed a structured procedure for predictive modelling, which includes data extraction and preprocessing, model adjustment and performance assessment, selection of the best models and interpretation of results. The dataset contained a total number of 238,590 laboratory records. Each record 27 laboratory tests, age, gender and presence of retinopathy or nephropathy). The records included information about 5846 patients with diabetes. Diagnosis served as a source of information about the target class values for classification. Results: It was discovered that inclusion of two expressions, namely "nephropathy" and "retinopathy" allows to increase the performance, achieving up to 79.82% precision, 81.52% recall, 80.64% F1 score, 82.61% accuracy, and 89.88% AUC using the neural network classifier. Additionally, different models showed different results in terms of interpretation significance: random forest confirmed that the most important risk factor for polyneuropathy is the increased neutrophil level, meaning the presence of inflammation in the body. Linear models showed linear dependencies of the presence of polyneuropathy on blood glucose levels, which is confirmed by the clinical interpretation of the importance of blood glucose control. Conclusion: Depending on whether one needs to identify pathophysiological mechanisms for one's prospective study or identify early or late predictors, the choice of model will vary. In comparison with the previous studies, our research makes a comprehensive comparison of different decisions using a large and well-structured dataset applied to different decision support tasks.

Distal Symmetric Polyneuropathy Identification in Type 2 Diabetes Subjects: A Random Forest Approach

“…They concluded that ANN provides a better performance obtaining 89.88% of Area Under the Curve. Likewise, Metsker et al [ 14 ] developed a structured procedure for predictive modeling, which includes data extraction, pre-processing, model adjustment, performance, and selection of the best models. The dataset comprises information about 5846 patients with diabetes.…”

Has the Flood Entered the Basement? A Systematic Literature Review about Machine Learning in Laboratory Medicine

“…In Reference [ 60 ], Metsker et al tackled the problem of predicting the risk of polyneuropathy in diabetic patients. To find the best way to handle missing data, they chose different solutions and obtained six different datasets.…”

“…Among the best models, those in the Ensemble family (e.g., XGB, GBT) were chosen both for their medium–high performance [ 21 , 30 , 32 , 33 , 44 , 51 , 58 , 61 ] and their training speed. Models in the DL family [ 27 , 35 , 37 , 49 , 52 , 53 , 60 ], especially RNN and ANN, have been increasingly chosen in recent years. The advantage of these systems is their potential in terms of performance, although the resources (time and the amount of data) required for training are reported to be higher for DL models than traditional ML models.…”

Prediction of Diabetic Sensorimotor Polyneuropathy Using Machine Learning Techniques

“…Although there is no standardized decision-making algorithm for DSPN diagnosis, HbA1c qualifies as an important diagnostic criterion for DPSN because HbA1c a major risk factor for microvascular complications and closely associated with DSPN in T2DM [ 64 ] The neutrophil-lymphocyte ratio is an inflammatory marker and an important factor that predicts cardiovascular disease [ 65 ] and foot ulcer infection [ 66 ] in diabetic patients. Neutrophil level was also the most sensitive node for decision making of DPSN prediction in a previous study [ 67 ], and higher neutrophil-lymphocyte ratio might be related to chronic inflammatory process and increase the risk of DSPN [ 68 ].…”