Evaluation of Risk Factors Associated With Antihypertensive Treatment Success Employing Data Mining Techniques.

OBJECTIVE: This study aimed to evaluate the effects of potential risk factors on antihypertensive treatment success. METHODS: Patients with hypertension who were treated with antihypertensive medications were included in this study. Data from the last visit were analyzed retrospectively for each patient. To evaluate the predictive models for antihypertensive treatment success, data mining algorithms (logistic regression, decision tree, random forest, and artificial neural network) using 5-fold cross-validation were applied. Additionally, study parameters between patients with controlled and uncontrolled hypertension were statistically compared and multiple regression analyses were conducted for secondary endpoints. RESULTS: The data of 592 patients were included in the analysis. The overall blood pressure control rate was 44%. The performance of random forest algorithm (accuracy = 97.46%, precision = 97.08%, F1 score = 97.04%) was slightly higher than other data mining algorithms including logistic regression (accuracy = 87.31%, precision = 86.21%, F1 score = 85.74%), decision tree (accuracy = 76.94%, precision = 70.64%, F1 score = 76.54%), and artificial neural network (accuracy = 86.47%, precision = 83.85%, F1 score = 84.86%). The top 5 important categorical variables (predictive correlation value) contributed the most to the prediction of antihypertensive treatment success were use of calcium channel blocker (-0.18), number of antihypertensive medications (0.18), female gender (0.10), alcohol use (-0.09) and attendance at regular follow up visits (0.09), respectively. The top 5 numerical variables contributed the most to the prediction of antihypertensive treatment success were blood urea nitrogen (-0.12), glucose (-0.12), hemoglobin A1c (-0.12), uric acid (-0.09) and creatinine (-0.07), respectively. According to the decision tree model; age, gender, regular attendance at follow-up visits, and diabetes status were identified as the most critical patterns for stratifying the patients. CONCLUSION: Data mining algorithms have the potential to produce predictive models for screening the antihypertensive treatment success. Further research on larger populations and longitudinal datasets are required to improve the models.

Is it possible to predict urodynamic stress urinary incontinence in women with minimal diagnostic evaluation?

OBJECTIVES: To determine whether it is possible to predict urodynamic stress urinary incontinence (uSUI) in women with minimal diagnostic evaluation. MATERIALS AND METHODS: Medical records of 2,643 female incontinent patients were reviewed and 301 women were eligible for this study. The positive predictive values (PPV), sensitivity, specificity and negative predictive values (NPV) for uSUI and uSUI with or without detrusor overactivity (DO), and DO patients of pure SUI symptom (group 1), combination of pure SUI symptom and positive provocative stress test (+PST; group 2) and combination of pure SUI symptom, +PST and absence of overactive bladder symptoms (group 3) were calculated for each group. RESULTS: Mean age was 51.03 years (22-88). PPV, sensitivity and specificity values for uSUI with or without DO of group 3 were 100, 7.4, and 100%, while these values for pure uSUI were 93.3, 9.3, and 99.3%, respectively. Interestingly, none of the patients in groups 2 and 3 had DO. CONCLUSIONS: Our results show that it was possible to predict uSUI with high accuracy using minimal diagnostic evaluation in a group of female patients with pure stress incontinence symptoms +PST while it was also possible to eliminate DO accurately in this group of patients.

Compression of the CT images using classified energy and pattern blocks.

In this work, a new biomedical image compression method is proposed based on the classified energy and pattern blocks (CEPB). CEPB based compression method is specifically applied on the Computed Tomography (CT) images and the evaluation results are presented. Essentially, the CEPB is uniquely designed and structured codebook which is located on the both the transmitter and receiver part of a communication system in order to implement encoding and decoding processes. The encoding parameters are block scaling coefficient (BSC) and the index numbers of energy (IE) and pattern blocks (IP) determined for each block of the input images based on the CEPB. The evaluation results show that the newly proposed method provides considerable image compression ratios and image quality.