Principal Component Analysis of Categorized Polytomous Variable-Based Classification of Diabetes and Other Chronic Diseases

Process mining and data mining applications in the domain of chronic diseases: A systematic review

The widespread use of information technology in healthcare leads to extensive data collection, which can be utilised to enhance patient care and manage chronic illnesses. Our objective is to summarise previous studies that have used data mining or process mining methods in the context of chronic diseases in order to identify research trends and future opportunities. The review covers articles that pertain to the application of data mining or process mining methods on chronic diseases that were published between 2000 and 2022. Articles were sourced from PubMed, Web of Science, EMBASE, and Google Scholar based on predetermined inclusion and exclusion criteria. A total of 71 articles met the inclusion criteria and were included in the review. Based on the literature review results, we detected a growing trend in the application of data mining methods in diabetes research. Additionally, a distinct increase in the use of process mining methods to model clinical pathways in cancer research was observed. Frequently, this takes the form of a collaborative integration of process mining, data mining, and traditional statistical methods. In light of this collaborative approach, the meticulous selection of statistical methods based on their underlying assumptions is essential when integrating these traditional methods with process mining and data mining methods. Another notable challenge is the lack of standardised guidelines for reporting process mining studies in the medical field. Furthermore, there is a pressing need to enhance the clinical interpretation of data mining and process mining results.

Machine Learning Techniques for Antimicrobial Resistance Prediction of Pseudomonas Aeruginosa from Whole Genome Sequence Data

AIM

Due to the growing availability of genomic datasets, machine learning models have shown impressive diagnostic potential in identifying emerging and reemerging pathogens. This study aims to use machine learning techniques to develop and compare a model for predicting bacterial resistance to a panel of 12 classes of antibiotics using whole genome sequence (WGS) data of Pseudomonas aeruginosa.

METHOD

A machine learning technique called Random Forest (RF) and BioWeka was used for classification accuracy assessment and logistic regression (LR) for statistical analysis.

RESULTS

Our results show 44.66% of isolates were resistant to twelve antimicrobial agents and 55.33% were sensitive. The mean classification accuracy was obtained ≥98% for BioWeka and ≥96 for RF on these families of antimicrobials. Where ampicillin was 99.31% and 94.00%, amoxicillin was 99.02% and 95.21%, meropenem was 98.27% and 96.63%, cefepime was 99.73% and 98.34%, fosfomycin was 96.44% and 99.23%, ceftazidime was 98.63% and 94.31%, chloramphenicol was 98.71% and 96.00%, erythromycin was 95.76% and 97.63%, tetracycline was 99.27% and 98.25%, gentamycin was 98.00% and 97.30%, butirosin was 99.57% and 98.03%, and ciprofloxacin was 96.17% and 98.97% with 10-fold-cross validation. In addition, out of twelve, eight drugs have found no false-positive and false-negative bacterial strains.

CONCLUSION

The ability to accurately detect antibiotic resistance could help clinicians make educated decisions about empiric therapy based on the local antibiotic resistance pattern. Moreover, infection prevention may have major consequences if such prescribing practices become widespread for human health.

Shared Physiologic Pathways Among Comorbidities for Adults With Cerebral Palsy

Objective: Aging with cerebral palsy is accompanied by a declining health and function status across neurological and non-neurological systems. There is a need to understand the shared pathophysiology among comorbidities for adults with cerebral palsy, to inform clinical assessment and guidelines for interventions to improve healthful aging. To begin defining multimorbidity, this study identified the most common comorbidity combinations and their association with mortality among a representative sample of adults with cerebral palsy.

Methods: Data from 2016 to 2018 were used from a random 20% sample from the fee-for-service Medicare database. Adults ≥18 years with cerebral palsy and 25 neurological and non-neurological comorbidities were obtained from 2016. Principal component (PC) analysis identified the most common comorbidity combinations, defined as individual PCs. Cox regression estimated the hazard ratio (HR) of 2-year mortality including all PCs and demographics in a single model. To facilitate comparisons, PC scores were transformed into quintiles (reference: lowest quintile).

Results: Among the 16,728 adults with cerebral palsy, the most common comorbidity combinations (PCs) in order were: cardiorespiratory diseases, dysphagia, and fluid/electrolyte disorders; metabolic disorders (e.g., diabetes, renal disease, hypertension); neurologic-related disorders (e.g., dementia, cerebrovascular disease); gastrointestinal issues; and orthopedic-related disorders. During the 2-year follow-up, 1,486 (8.9%) died. In the adjusted model, most PCs were associated with an elevated mortality rate, especially the first PC (5th quintile HR = 3.91; 95%CI = 3.29–4.65).

Discussion: This study identified the most common comorbidity combinations for adults with cerebral palsy, many of them were deadly, which may inform on the underlying pathophysiology or shared characteristics of multimorbidity for this population.

Analysis of the Statistical Comparability of the Hardness and Wear of Polymeric Materials for Orthodontic Applications

Background: Clinical success depends on the contact strength and wear resistance of medical devices made of polymer materials. The scientific goal resulted from the problem of using different methods of surface evaluation of materials used in the production of orthodontic appliances. The purpose of the work was an experimental comparative assessment of indentation hardness and scratch hardness and the sliding wear of four selected polymeric materials used in the manufacture of orthodontic appliances. Methods: Four commercial materials were compared. Shore hardness tests and a scratch test with a Rockwell indenter were performed. A sliding wear test was performed using the ball-on-disc method. Statistical PCA and correlation analyses were performed. Results: The results of scratch hardness measurements using a contact profilometer correlated with the Shore hardness to a greater extent than measurements made using an optical microscope. PCA showed that Shore hardness explains 45% of the total variance in all the results across the materials. Conclusions: The scratch hardness method allows for a more explicit ranking of orthodontic polymeric materials when measurements are made with a profilometer. The ranking of sliding wear resistance should be made separately.

An Empirical Study on Diabetes Depression over Distress Evaluation Using Diagnosis Statistical Manual and Chi-Square Method

Diabetes distress is an alternative disorder that is often associated with depression syndromes. Psychosocial distress is an alternative disorder that acts as a resistance to diabetes self-care management and compromises diabetes control. Yet, in Nigeria, the focus of healthcare centers is largely inclined toward the medical aspect of diabetes that neglects psychosocial care. In this retrospective study, specific distress was measured by the Diabetes Distress Screening (DDS) scale, and depression was analyzed by the Beck Depression Inventory (BDI) and Diagnosis Statistics Manual (DSM) criteria in type 2 diabetes mellitus (T2DM) patients of Northwestern Nigeria. Additionally, we applied the Chi-square test and linear regression to measure the forecast prevalence ratio and evaluate the link between the respective factors that further determine the odd ratios and coefficient correlations in five nonintrusive variables, namely age, gender, physical exercise, diabetes history, and smoking. In total, 712 sample patients were taken, with 51.68% male and 47.31% female patients. The mean age and body mass index (BMI) was 48.6 years ± 12.8 and 45.6 years ± 8.3. Based on the BDI prediction, 90.15% of patients were found depressed according to the DSM parameters, and depression prevalence was recorded around 22.06%. Overall, 88.20% of patients had DDS-dependent diabetes-specific distress with a prevalence ratio of 24.08%, of whom 45.86% were moderate and 54.14% serious. In sharp contrast, emotion-related distress of 28.96% was found compared to interpersonal (23.61%), followed by physician (16.42%) and regimen (13.21%) distress. The BDI-based matching of depression signs was also statistically significant with p < 0.001 in severe distress patients. However, 10.11% of patients were considered not to be depressed by DSM guidelines. The statistical evidence indicates that depression and distress are closely correlated with age, sex, diabetes history, physical exercise, and smoking influences. The facts and findings in this work show that emotional distress was found more prevalent. This study is significant because it considered several sociocultural and religious differences between Nigeria and large, undeveloped, populated countries with low socioeconomic status and excessive epidemiological risk. Finally, it is important for the clinical implications of T2DM patients on their initial screenings.

The effect of collaborative care model training on diabetic foot ulcer patients' quality of life: a semi-experimental study

Purpose

The aim of this semi-experimental study was the determination of collaborative care model training on diabetic foot ulcer patients' quality of life.

Methods

Demographic characteristics and life's quality questionnaires were used, and to implement the collaborative model that included (including four steps of motivation, preparation, engagement, and evaluation), 60 patients divided into two "experiment" and "control" groups. For the experimental group, the program was done by face to face education and pamphlet through one hour in seven sessions. Data were analyzed by descriptive and inferential statistical methods. SPSS 24 was used for data analysis.

Results

Findings showed the differences of the scores of life's quality dimensions (except the economic situation), which was significant between both groups after the implementation of the collaborative care model (p < 0.001).

Conclusion

The implementation of collaborative care model can be effective to improve diabetic foot ulcer patients' quality of life.

Stratified Diabetes Mellitus Prevalence for the Northwestern Nigerian States, a Data Mining Approach

An accurate classification for diabetes mellitus (DBM) allows for the adequate treatment and handling of its menace, particularly in developing countries like Nigeria. This study proposes data mining techniques for the classification and identification of the prevalence of diagnosed diabetes cases, stratified by age, gender, diabetic conditions and residential area in the northwestern states of Nigeria, based on the real-life data derived from government-owned hospitals in the region. A K-mean assessment was used to cluster the instances, after 12 iterations the instances classified out of 3022: 2662 (88.09%) non-insulin dependent (NID), 176 (5.82%) insulin-dependent (IND) and 184 (6.09%) gestational diabetes (GTD). The total number of diagnosed diabetes cases was 3022: 1380 males (45.66%) and 1642 females (54.33%). The higher prevalence was found to be in females compared to males, and in cities and towns, rather than in villages (36.5%, 34.2%, and 29.3%, respectively). The highest prevalence among the age groups was in the age group 50-69 years, which constituted 43.9% of the total diagnosed cases. Furthermore, the NID condition had the highest prevalence of cases (88.09%). These were the first findings of the stratified prevalence in the region, and the figures have been of utmost significance to the healthcare authorities, policymakers, clinicians, and non-governmental organizations for the proper planning and management of diabetes mellitus.