On statistical analysis methods improving epidemiological studies

Nowadays almost all the principles of diagnosis and treatment are determined by the statistical analysis of the observations made by practitioners. The analysis of medical data is therefore one of the most important elements that affect the level of modern medical care. The research described in this paper aims to determine the characteristics of cardiac parameters in healthy children and in children with a diagnosis in arterial hypertension. Studies include children in the Lodz region. The purpose of the analysis is to determine risk factors of arterial hypertension and thus early diagnosis of children and as quickly as possible the inclusion of an appropriate treatment or observation. The analysis applies a number of methods, including descriptive analysis, grouping and statistical inference. The choice of methods is consistent with the requirements of the USMLE (The United States Medical Licensing Examination) and depends on the evaluated parameters. The research is carried out using professional statistical packages and the computer system designed and developed for this study. The use this system allows for the continuous process of research staging of new cases, which is a definite advantage when conducting epidemiological studies

Feature Selection and Classification Pairwise Combinations for High-dimensional Tumour Biomedical Datasets

This paper concerns classification of high-dimensional yet small sample size biomedical data and feature selection aimed at reducing dimensionality of the microarray data. The research presents a comparison of pairwise combinations of six classification strategies, including decision trees, logistic model trees, Bayes network, Na¨ıve Bayes, k-nearest neighbours and sequential minimal optimization algorithm for training support vector machines, as well as seven attribute selection methods: Correlation-based Feature Selection, chi-squared, information gain, gain ratio, symmetrical uncertainty, ReliefF and SVM-RFE (Support Vector Machine-Recursive Feature Elimination). In this paper, SVMRFE feature selection technique combined with SMO classifier has demonstrated its potential ability to accurately and efficiently classify both binary and multiclass high-dimensional sets of tumour specimens

The analysis of vaginal hysterectomy results depending on the uterine size

Objectives: Vaginal hysterectomy is one of the oldest but still rarely used minimally invasive techniques. Although new surgical methods making use of robots in laparoscopy have been introduced recently, when compared with vaginal hysterectomy, these approaches do not offer significant benefits for the patients and the doctors operating on them. The purpose of this study was a thorough analysis of vaginal removal of non-prolapsed uterus with benign pathology.Material and methods: The analysis included data of 1148 women who underwent vaginal hysterectomy in the Clinic of Surgical, Endoscopic and Oncological Gynecology between 2002 and 2014. A group of patients operated on were assessed, and data from the surgeries were obtained paying attention to such aspects as the operating time, the evaluation of morphotic blood elements, the type of perioperative complications, and the length of postoperative hospital stay. Additionally, all vaginal hysterectomies were divided into groups and analyzed taking into consideration uterus weight.Results: Vaginal hysterectomy was performed even in cases of earlier abdominal surgeries. The mean operating time was and 69.51 ± 28.32 minutes. The patients left hospital after 2.93 days on average. The mean uterus weight was 179.69 ± 113.54 g. What is important, the enlarged uterus was not a significant obstacle during the surgery. In case of heavy uteri of more than 580g, when the fundus of the uterus reached above the navel, the attention was drawn to the need for careful preparatory procedures, which reduced the number of perioperative complications and thus had a significant influence on the length of the operation (p = 0.0170).Conclusions: Vaginal hysterectomy is an operating technique which is relatively easy to perform and safe for the patients because it involves a slight decrease of morphotic blood elements and a small number of mid- and postoperative complications. Vaginal hysterectomy is not a contraindication in case of large uteri, even those of more than 1000 g; however, in such cases, a longer operating time and an increased number of perioperative complications must be taken into consideration

Evaluation of serum levels of soluble (s)L- and (s)P-selectins in endometrial cancer

Objectives: A number of reports on the role of selectin in the process of carcinogenesis, at the stage of proliferation and metastasis, have been available. The aim of the study was to analyze (s)P- and (s)L-selectin serum concentrations in women with EC and to compare these concentrations to clinical/pathological parameters and disease progression using surgical-pathological staging data. Material and methods: A total of 46 patients with EC and 50 healthy controls were included in the study. Serum concentrations of sL- and sP-selectins were measured in all participants. The oncologic protocol was implemented in all women from the study group. Results: Significantly higher serum concentrations were found in EC women as compared to controls. No statistically significant differences were found between the concentrations of the soluble forms of selectins and the following parameters: histologic type of EC, histologic tumor differentiation, depth of myometrial infiltration, cervical involvement, distant metastases, vascular space invasion, and disease advancement. Slightly higher (s)P-selectin concentrations were observed in serous carcinoma, in women with cervical involvement, in the sera of women with vascular space invasion and with advanced stages of the disease. Slightly higher mean (s)P-selectin concentrations correlated with lower differentiation of the tumor. Slightly higher mean (s)P-selectin concentration was detected in the sera of women with lymph node metastases and with the serosal and/or adnexal involvement. The results were statistically insignificant, but they almost reached statistical significance. Conclusions: L- and P-selectins play a role in the biology of EC. The absence of an unambiguous relationship between differences in (s)L- and (s)P-selectin levels and disease advancement suggests that they do not play a vital role in tumor progression in endometrial cancer

Neonatal survival and kidney function after prenatal interventions for obstructive uropathies

Objectives: Prenatal interventions in LUTO (lower urinary tract obstruction) usually are still question of a debate between gynaecologist and paediatric nephrologist. We aimed the study to assess the early survival rate and renal outcome in LUTO foetuses. Material and methods: The study was a prospective data analysis of 39 foetuses from singleton pregnancies. All pregnant women with LUTO in the foetus were qualified for VAS based on a local practice. The mean time of first urine analysis ranged between 13–30 weeks of pregnancy. Primary end-point analysis included live birth, 28d-survival, pulmonary and renal function assessment in neonatal period. Results: From initial number of 39, six patients miscarried before the procedure was performed. Overall, 33 VAS were performer at the mean 21 week of pregnancy (range 14–30 weeks). 25/39 foetuses survived until delivery. Three neonates died in first 3 days of life. In the first month 3 children required peritoneal dialysis, but at 28 day all children were dialysis-free. Overall survival rate at 28 day was 56%. Renal function preservation of the initial group (39) turned out to be low — 18% (7/39). Conclusions: Our study showed average survival curves and complications. LUTO in the foetus had mostly unfavourable outcome in the neonatal period. The prenatal intervention did not increase it significantly and did not guarantee the preservation of normal kidney function

Children with monosymptomatic primary nocturnal enuresis - the clinical profile of patients during the first nephrological consultation

Introduction: Nocturnal enuresis can be diagnosed in a child older than 5 years of age who involuntarily discharges urine in the wrong time, i.e. at night, and in the wrong place, i.e. in bed. Aim: The aim of the study was to analyse the profile of patients who consult the specialist physician with monosymptomatic primary nocturnal enuresis. Material and methods: The data were collected from a questionnaire completed by guardians of children during the first nephrological consultation. The questions concerned the following: family history of nocturnal enuresis, bedwetting intensity, other urinary tract symptoms, a voiding chart and fluid intake record, number of nocturnal enuresis incidents in 14 days, episodes of nocturia, nocturnal diuresis volume, urinary urgency volume and constipation. Moreover, basic anthropometric measurements were taken. The data were analysed and the following values were calculated: average voided volumes, maximum voided volumes, voided volumes before 5 p.m. and 24-hour diuresis. An analogous analysis was conducted with respect to fluid intake. Results: Most patients were males. The family history of nocturnal enuresis was positive in ⅓ of patients. Approximately ⅓ of patients tended to drink fluids directly before bedtime. The number of patients with sporadic nocturnal enuresis (23–45%) was comparable to the number of patients with frequent nocturnal enuresis (28–55%). Nocturnal diuresis suggested nocturnal polyuria in 11 patients (21.6%). Decreased functional bladder capacity was found in almost ¼ of patients (12–23.5%). Conclusions: Monosymptomatic nocturnal enuresis is more common in boys. The family history was positive in ⅓ of patients. Patients and their guardians are not aware of fluid intake restrictions at bedtime. The frequency of nocturnal polyuria and decreased functional bladder capacity is comparable in the investigated patients

Principal Component Analysis based on data characteristics for dimensionality reduction of ECG recordings in arrhythmia classification

Due to the growing problem of heart diseases, the computer improvement of their diagnostics becomes of great importance. One of the most common heart diseases is cardiac arrhythmia. It is usually diagnosed by measuring the heart activity using electrocardiograph (ECG) and collecting the data as multidimensional medical datasets. However, their storage, analysis and knowledge extraction become highly complex issues. Feature reduction not only enables saving storage and computing resources, but it primarily makes the process of data interpretation more comprehensive. In the paper the new igPCA (in-group Principal Component Analysis) method for feature reduction is proposed. We assume that the set of attributes can be split into subgroups of similar characteristic and then subjected to principal component analysis. The presented method transforms the feature space into a lower dimension and gives the insight into intrinsic structure of data. The method has been verified by experiments done on a dataset of ECG recordings. The obtained effects have been evaluated regarding the number of kept features and classification accuracy of arrhythmia types. Experiment results showed the advantage of the presented method compared to base PCA approach

Hybrid Method of Automated EEG Signals’ Selection Using Reversed Correlation Algorithm for Improved Classification of Emotions

Based on the growing interest in encephalography to enhance human–computer interaction (HCI) and develop brain–computer interfaces (BCIs) for control and monitoring applications, efficient information retrieval from EEG sensors is of great importance. It is difficult due to noise from the internal and external artifacts and physiological interferences. The enhancement of the EEG-based emotion recognition processes can be achieved by selecting features that should be taken into account in further analysis. Therefore, the automatic feature selection of EEG signals is an important research area. We propose a multistep hybrid approach incorporating the Reversed Correlation Algorithm for automated frequency band—electrode combinations selection. Our method is simple to use and significantly reduces the number of sensors to only three channels. The proposed method has been verified by experiments performed on the DEAP dataset. The obtained effects have been evaluated regarding the accuracy of two emotions—valence and arousal. In comparison to other research studies, our method achieved classification results that were 4.20–8.44% greater. Moreover, it can be perceived as a universal EEG signal classification technique, as it belongs to unsupervised methods

Integrating Correlation-Based Feature Selection and Clustering for Improved Cardiovascular Disease Diagnosis

Based on the growing problem of heart diseases, their efficient diagnosis is of great importance to the modern world. Statistical inference is the tool that most physicians use for diagnosis, though in many cases it does not appear powerful enough. Clustering of patient instances allows finding out groups for which statistical models can be built more efficiently. However, the performance of such an approach depends on the features used as clustering attributes. In this paper, the methodology that consists of combining unsupervised feature selection and grouping to improve the performance of statistical analysis is considered. We assume that the set of attributes used in clustering and statistical analysis phases should be different and not correlated. Thus, the method consisting of selecting reversed correlated features as attributes of cluster analysis is considered. The proposed methodology has been verified by experiments done on three real datasets of cardiovascular cases. The obtained effects have been evaluated regarding the number of detected dependencies between parameters. Experiment results showed the advantage of the presented approach compared to other feature selection methods and without using clustering to support statistical inference