Polyneuropathy disease forecast in the type 2 diabetes mellitus patients using data mining based approach

Abstract

Aim: The aim of this research was to predict the availability of polyneuropathy disease of the type 2 diabetes mellitus patient through data mining algorithms. Material and Method: The dataset was obtained from the Bilecik Public Hospital and the instance number is 2907. Models were created with two different classification data mining algorithms. The data set includes Gender, Glycated Haemoglobin (HbA1c), Creatinine, Total Cholesterol, Low-Density Lipoprotein (LDL) and High-Density Lipoprotein (HDL). Numerical data were transformed into interval forms and the percentiles were calculated for each interval. Results: Data analysis and performance evaluation were performed with R, RStudio. Random Forest Tree was found as the best algorithm for polyneuropathy disease prediction (the accuracy = 0,922547332185886). The accuracy of the C4.5 was found 0,920826161790017. The percentages of the normal levels HbA1c are 6%, the impaired fasting glucose levels are 22% and the diabetes mellitus type 2 levels are % 72. The percentage of the low Creatinine is 2%, the normal Creatinine is % 86 and the high Creatinine is 12%. The percentage of the desirable levels Total Cholesterol is 46%, the percentage of the borderline levels of Total Cholesterol is 31% and the the percentage of high levels of Total Cholesterol is 23%. The percentage of the optimal levels LDL is 30%, the percentage of the near optimal levels of LDL is 31%, the percentage of the borderline high levels of LDL is 23%, the percentage of the high levels of LDL is 12% and the percentage of the very high levels of LDL is 4%. The percentage of the bad levels of HDL is 43%, the percentage of the better levels of HDL is 42% and the percentage of the best levels HDL is 19%. This model indicated that Creatinine, LDL and HbA1c are the primary three determinative factors on polyneuropathy disease. Furthermore, the model created the following 5 rules. Rule 1: If Creatinine = 0,6 mg/dL and HbA1c = 5,7 mmol/L then polyneuropathy disease is available for male, if Creatinine = 0,5 mg/dL and HbA1c = 5,7 mmol/L then polyneuropathy disease is available for female. Rule 2: If Creatinine = 0,5 mg/dL and HbA1c > 5,7 mmol/L and 160 = LDL = 189 mg/dL and LDL = 189 mg/dL then polyneuropathy disease is unavailable. Rule 3: If Creatinine = 0,5 mg/dL and HbA1c > 5,7 mmol/L and 160 = LDL = 189 mg/dL and LDL > 189 mg/dL, then polyneuropathy disease is unavailable. Rule 4: If Creatinine = 0,5 mg/dL and HbA1c > 5,7 mmol/L and 160 = LDL = 189 mg/dL, then polyneuropathy disease is unavailable. Rule 5: If Creatinine > 0,5 mg/dL then polyneuropathy disease is unavailable. Discussion: The results show that HDL, Gender and Total Cholesterol have no significant effect on the polyneuropathy disease in this model. To determine the availability of polyneuropathy disease through the given data mining algorithms, researchers may consider the Creatinine, LDL and HbA1c scores.