logistic regression

COVID-19 remains a global health challenge, with vaccination crucial for reducing severe cases. This study evaluated a two-dose COVID-19 vaccine’s effectiveness in lowering hospitalization rates using advanced statistical techniques. This study evaluated the efficacy of a two-dose COVID-19 vaccination regimen in reducing hospitalization rates by employing advanced statistical techniques to control confounding variables in the observational data.

A retrospective cohort study was conducted among individuals tested for COVID-19 at Mashhad University of Medical Sciences from March 21, 2021, to March 20, 2022. The study population comprised all individuals who underwent polymerase chain reaction testing for COVID-19 during this period. A census sampling method was employed, resulting in a final sample size of 306630 individuals. The participants were classified as “vaccinated” if they received both doses and “unvaccinated” if they received none. Hospitalization was defined as COVID-19-related admissions occurring at least two weeks post-vaccination. The required data were collected from three databases, including the Sina Health Information System, the Healthcare Services Monitoring System, and the Hospital Information System. To create comparable groups, propensity score (PS) matching and weighting were utilized, and a logistic regression model was utilized to estimate the average treatment effect (ATE) of vaccination on hospitalization outcomes.

Among the 306630 patients included in the study, 104115 (33.95%) were unvaccinated, while 202515 (66.05%) were vaccinated. Overall, 29458 patients (9.61%) were hospitalized, comprising 28,244 unvaccinated and 1214 vaccinated individuals. Vaccinated individuals exhibited significantly lower odds of hospitalization. The adjusted odds ratio for hospitalization was 0.72 (95% confidence interval [CI]: 0.68–0.76) when using PS weighting, 0.32 (95% CI: 0.30–0.34) with matching, and 0.34 (95% CI: 0.33–0.35) after adjusting for extreme weights.

The findings underscore the protective effects associated with a two-dose COVID-19 vaccination regimen and emphasize the significance of employing robust statistical methods in evaluating real-world data.

To estimate the effectiveness of two-dose COVID-19 vaccination in reducing hospitalization, accounting for complex confounding factors in observational studies.

Researchers applied propensity score methods to adjust for confounding variables, comparing their performance to traditional covariate adjustment methods. Multiple Logistic Regression and Propensity Score Matching were employed to analyze the data, ensuring a balanced comparison between vaccinated and unvaccinated groups.

Both analytical methods demonstrated a significant reduction in the likelihood of hospitalization among vaccinated individuals. The adjusted odds ratios (OR) were 0.29 (95% CI: 0.26, 0.31) via logistic regression and 0.32 (95% CI: 0.30, 0.34) using propensity score matching.

The study confirms the effectiveness of two-dose COVID-19 vaccination in decreasing hospitalization. It highlights the importance of using meticulous approaches like propensity score methods to assess real-world impacts in complex observational data settings.

 Metabolic dysfunction-associated steatotic liver disease (MASLD) represents a significant global health burden without established curative therapies. Early detection and preventive strategies are crucial for effective MASLD management. This study aimed to develop and validate machine-learning (ML) algorithms for accurate MASLD screening in a geographically diverse, large-scale population.

 Data from the prospective Fasa Cohort Study, initiated in rural Fars province, Iran (March 2014), were employed for this purpose. The required data were collected using blood tests, questionnaires, liver ultrasonography, and physical examinations. A two-step approach identified key predictors from over 100 variables: (1) statistical selection using mean decrease Gini in random forest and (2) incorporation of clinical expertise for alignment with known MASLD risk factors. The hold-out validation approach (with a 70/30 train/validation split) was utilized, along with 5-fold cross-validation on the validation set. Logistic regression, Naïve Bayes, support vector machine, and light gradient-boosting machine (LightGBM) algorithms were compared for model construction with the same input variables based on area under the receiver operating characteristic curve (AUC), sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and accuracy.

 A total of 6,180 adults (52.7% female) were included in the study, categorized into 4816 non-MASLD and 1364 MASLD cases with a mean age (±standard deviation [SD]) of 48.12 (±9.61) and 49.47 (±9.15) years, respectively. Logistic regression outperformed other ML algorithms, achieving an accuracy of 0.88 (95% confidence interval [CI]: 0.86-0.89) and an AUC of 0.92 (95% CI: 0.90-0.93). Among more than 100 variables, the key predictors included waist circumference, body mass index (BMI), hip circumference, wrist circumference, alanine aminotransferase levels, cholesterol, glucose, high-density lipoprotein, and blood pressure.

 Integration of ML in MASLD management holds significant promise, particularly in resource-limited rural settings. Additionally, the relative importance assigned to each predictor, particularly prominent contributors such as waist circumference and BMI, offers valuable insights into MASLD prevention, diagnosis, and treatment strategies.

Thalassemia is an inherited blood disorder with a defect in the sufficient production of a protein called hemoglobin. We aimed to investigate the simple blood indices of patients with Beta Thalassemia Trait (BTT) and Iron Deficiency Anemia (IDA) to propose a new formula using logistic regression for differentiate two characteristics from each other.

Among the 702 records of the BTT Counseling Center (Khoy, Iran-2022), 292 cases (219 iron deficiency anemia (IDA) and 73 BTT) were eligible for the study. Blood indices such as RBC, HGB, HbA2 described and used to diagnose two types of participants. Blood indices had high multicollinearity that was modified. Logistic regression for blood indices fitted and goodness of fit indices with Area Under ROC curve (AUC) estimated.

The average age of the participants was 24.56 yr. The status of Multicollinearity between independent variables was modified. The HGB, MCV, HbA2, and HbA variables were used in the model and only HbA2 status was significant (P<0.001). According to the output of the model, for each unit increase in HbA2, the chance of having BTT was about 8.5 times higher than IDA. The sensitivity, specificity, AUC curve, and accuracy of the final model were estimated to be 97, 72, 84, and 93%, respectively. A regression formula to differentiate BTT from IDA proposed.

In studies related to the differentiation of the BTT from IDA, the presence of the HbA2 index in the model and prediction is very necessary.

This study aims to identify the risk factors affecting mortality among Covid-19 patients in the southeast of Iran. This cross-sectional study used data from the Covid-19 patients admitted to Afzalipur Teaching Hospital in Kerman, Iran, from February 2020 to September 2021. The demographic and clinical data of 6,057 patients were analyzed using Bayesian network and logistic regression models. Out of 6,057 patients, 333 patients (5.5%) died. The most important risk factors for Covid-19 mortality were age, gender, fever, headache, decreased level of consciousness (LoC), chronic liver disease, blood oxygen level (BOL), admission season, and length of stay (LoS). Fever, headache, and longer LoS were protective and mortality-reducing variables. Following model estimation results, it is recommended that old male patients with low oxygen levels and a lower LoC, as well as patients with chronic liver disease, receive additional medical care and not be discharged prematurely. Early medical interventions for high-risk patients may reduce the Covid-19 mortality risk.

Although several studies have been published about COVID-19, ischemic stroke is known yet as a complicated problem for COVID-19 patients. Scientific reports have indicated that in many cases, the incidence of stroke in patients with COVID-19 leads to death. 

The obtained mathematical equation in this study can help physicians’ decision-making about treatment and identification of influential clinical factors for early diagnosis. 

In this retrospective study, data from 128 patients between March and September 2020, including their demographic information, clinical characteristics, and laboratory parameters were collected and analyzed statistically. A logistic regression model was developed to identify the significant variables in predicting stroke incidence in patients with COVID-19. 

Clinical characteristics and laboratory parameters for 128 patients (including 76 males and 52 females; with a mean age of 57.109±15.97 years) were considered as the inputs that included ventilator dependence, comorbidities, and laboratory tests, including WBC, neutrophil, lymphocyte, platelet count, C-reactive protein, blood urea nitrogen, alanine transaminase (ALT), aspartate transaminase (AST) and lactate dehydrogenase (LDH). Receiver operating characteristic–area under the curve (ROC-AUC), accuracy, sensitivity, and specificity were considered indices to determine the model capability. The accuracy of the model classification was also addressed by 93.8%. The area under the curve was 97.5% with a 95% CI.

The findings showed that ventilator dependence, cardiac ejection fraction, and LDH are associated with the occurrence of stroke and the proposed model can predict the stroke effectively.

Identifying effective symptoms, demographic information, and underlying diseases to predict COVID-19 mortality is essential. We aimed to study the effective clinical and symptomatic characteristics of COVID-19 mortality in hospitalized patients with positive polymerase chain reaction (PCR) test results.

For this study, we prospectively collected complete data on 26867 hospitalized individuals who tested PCR positive for COVID-19 from February 20, 2020, to September 12, 2021, in the Khorasan Razavi Province, Iran. We analyzed the data using artificial neural networks (ANN) and logistic regression (LR) models.

The accuracy of the ANN model was higher than the LR (90.27% versus 90.15%). The ten most important predictors that contributed to the prediction of death were decreasing consciousness level, cough, PO2 level, age, chronic kidney disease, fever, headache, smoking status, chronic blood diseases, and diarrhea using the ANN model.

In conclusion, individuals suffering from chronic diseases such as cancer, kidney and blood diseases, as well as immunodeficiency are at a higher risk of mortality. This important finding can help decision-makers and medical professionals in their efforts to consider these conditions and provide effective preventative measures to reduce the risk of death.

In this specific research study, a remarkably accurate and significantly simplified approach has been presented.

This research is encompasses three crucial stages. Firstly, the length of the signal is effectively diminished to an optimal magnitude through the utilization of a technique widely known as windowing. This technique plays a pivotal role in reducing the signal to an ideal size, ensuring the subsequent stages are executed with utmost precision. Secondly, the pertinent features are extracted from the shortened signal, specifically focusing on the Fractal Dimension, the Hurst Exponent, and the Ratio of Determinism to Recurrence Rate. These features are chosen due to the inherent nonlinear nature of the signal, as they provide valuable insights into the complex patterns and structures present within. Lastly, the extracted features undergo Logistic Regression, a widely employed classification algorithm, to effectively categorize and classify them. This step plays a crucial role in providing a clear and concise understanding of the underlying characteristics of the signal.

The implementation of the proposed method not only achieves an outstanding accuracy rate of 99.66%, but it also exhibits a linear time complexity, ensuring efficient processing. Additionally, this method leads to a significant reduction in the length of EEG signals, which is of utmost importance in practical applications.

The primary objective of this proposed method revolves around the introduction of an online approach that can seamlessly integrate into healthcare systems. This objective is derived from a comprehensive analysis and evaluation of the obtained results, ensuring the method's practicality and effectiveness are thoroughly assessed.

The development of data mining techniques and the adaptive neuro-fuzzy inference system (ANFIS) in the last few decades has made it possible to achieve accurate predictions in medical fields.

The present study aimed to use the ANFIS model, artificial neural network (ANN), and logistic regression to predict thyroid patients.

This study aimed to predict thyroid disease using the UCI database, ANFIS and ANN models, and logistic regression. We only used four of its features as the input of the model and considered thyroid as a binary response (occurrence=1, non-occurrence=0) as the output of the model. Finally, three models were compared based on the accuracy and the area under the curve (AUC).

In this study, out of the extensive UCI database, which includes 3,772 samples and over 20 features, only five specific features were utilized. Data include 1,144 males and 2,485 females. The results of multiple logistic regression analysis demonstrated that free T4 index (FTI) and thyroid stimulating hormone (TSH) had a significant effect on thyroid. The ANFIS model had a higher accuracy (99%) compared to ANN (96%) and the logistic regression model (94%) in the prediction of thyroid.

As evidenced by the obtained results, the forecasting performance of ANFIS is more efficient than other models. Moreover, the use of combined methods, such as ANFIS, to diagnose and predict diseases increases the accuracy of the model. Therefore, the results of this study can be used for screening programs to identify people at risk of thyroid disease.

The objective of the present study was to identify prognostic factors associated with mortality and transfer to intensive care units (ICUs) in hospitalized COVID-19 patients using random forest (RF). Also, its performance was compared with logistic regression (LR).

In this retrospective cohort study, information of 329 COVID-19 patients were analyzed. These patients were hospitalized in Besat hospital in Hamadan province, the west of Iran. The RF and LR models were used for predicting mortality and transfer to ICUs. These models' performance was assessed using area under the receiver operating characteristic curve (AUC) and accuracy.

Of the 329 COVID-19 patients, 57 (15.5%) patients died and 106 (32.2%) patients were transferred to ICUs. Based on multiple LR model, there was a significant association between age (OR=1.02; 95% CI=1.00-1.05), cough (OR=0.24; 95% CI=0.10-0.56), and ICUs (OR=7.20; 95% CI=3.30-15.69) with death. Also, a significant association was found between kidney disease (OR=3.90; 95% CI=1.04-14.63), decreased sense of smell (OR=0.28; 95% CI=0.10-0.73), Kaletra (OR=2.53; 95% CI=1.39-4.59), and intubation (OR=8.32; 95% CI=3.80-18.24) with transfer to ICUs. RF showed that the order of variable importance has belonged to age, ICUs, and cough for predicting mortality; and age, intubation, and Kaletra for predicting transfer to ICUs.

This study showed that the performance of RF provided better results compared to LR for predicting mortality and ICUs transfer in hospitalized COVID-19 patients.

We aimed to establish and validate diagnostic models for distinguishing bacterial/viral infections among sepsis neonates and also a model for prognostic evaluation.

Training data sets (cohorts) of neonatal sepsis patients were derived retrospectively from 2017 to 2019, and the verifying sets were followed up from 2019 to 2021. The backward elimination method of logistic regression was used in identifying the optimum feature combination by adding all potential factors to the regression equation.

The current study established 3 models. For distinguishing bacterial sepsis patientsandbacterial culture-negative patients, we found Y=1.930+0.105X1+0.891X2-1.389X3-0.774X4 (Y symbolizes the status of bacterial infectious sepsis, X1 is age increase, X2 is intra-amniotic infection (mother), X3 is vomiting sign, and X4 is cough sign). Similarly, for distinguishing bacterial infectious sepsis patients and bacterial/viral double-positive patients, we found Y=2.918+1.568X1+1.882X2-0.113X3-2.214X4-2.255X5-2.312X6 (Y means the bacterial/viral double-positive status, X1 is IL-6 increase, X2 means CRP increase, X3 means age increase, X4 means high fever sign, X5 is cyanotic sign, and X6 is HGB increase). For predicting hospital days as one of the prognoses, we found Y=-1.993+0.073X1+1.963X2+0.466X3-0.791X4-0.633X5 (Y means worse prognosis, which is hospital days longer than 7 days, X1 means age increase, X2 means intra-amniotic infection (mother), X3 is IL-6 increase, X4 is convulsion with unconsciousness, and X5 is cough sign). Then, the ROC curves of the models from the verifying cohort indicated that all of the 3 models had good performance among sepsis children.

Two diagnostic models and one prognostic model were established for clinical reference from the current first-step analysis with excellent model performance, which could be suggested as new useful diagnostic tools and a therapeutic strategy guiding marker for neonatal sepsis in the future.

We aimed to use polymerase chain reaction (PCR) on genomic deoxyribonucleic acid (DNA) to detect the Db allele and the rs2923234 and rs1049112 single nucleotide polymorphisms (SNPs) of the salivary acidic proline-rich proteins (PRPs) to determine their relationship with dental caries in young children.

DNA was extracted from saliva samples of preschool children aged 3 to 5 years. PCR primers designed around exon 3 of the PRH1 locus yielded a 416-base product representing Db for gel electrophoresis and a 519-base product representing the rs2923234 and rs1049112 SNPs for Sanger sequencing. The data were analyzed using a logistic regression model and a multilayer perceptron artificial neural network.

Forty children with severe caries and 40 caries-free children completed the study. The frequency of the Db gene was 16.3% in the entire study group. The rs2923234 SNP was a marginally significant (P=0.053) predictor for the dependent variable (caries-free or severe caries). However, the rs1049112 (P=0.407) and the Db allele (P=0.442) were not significant predictors.

The rs29232334 SNP could be considered a potential genetic predictor for caries susceptibility.

The accurate diagnosis of cardiac disease is vital in managing patients’ health. Data mining and machine learning techniques play an important role in the diagnosis of heart disease. We aimed to examine the diagnostic performances of an adaptive neuro-fuzzy inference system (ANFIS) for predicting coronary artery disease and compare this with two statistical methods flexible discriminant analysis (FDA) and logistic regression (LR).  

The data of this study is the result of descriptive-analytical research from the study of Mashhad. We used ANFIS, LR, and FDA to predict coronary artery disease. A total of 7385 subjects were recruited as part of the Mashhad Stroke and Heart Atherosclerotic Disorders (MASHAD) cohort study. The data set contained demographic, serum biochemical parameters, anthropometric, and many other variables. To evaluate the ability of trained ANFIS, LR, and FDA models to diagnose coronary artery disease, we used the Hold-Out method.For analyzing data, we used SPSS v25, R 4.0.4, and MATLAB 2018 software.  

The accuracy, sensitivity, specificity, Mean squared error (MSE)‎ , and area under the roc curve (AUC) for ANFIS were 83.4%, 80%, 86%, 0.166 and 83.4%. The corresponding values based on the LR method were 72.4%, 74%, 70% , 0.175 and 81.5% and for the FDA method, these measurements were 77.7%, 74%, 81%, 0.223, and 77.6%, respectively.  

There was a significant difference between the accuracy of these three methods. The present findings showed that ANFIS was the most accurate method for diagnosing coronary artery disease compared with LR and FDA methods. Thus, it could be a helpful tool to aid medical decision-making for the diagnosis of coronary artery disease.

Breast cancer is one of the most common cancers among women compared to all other ones. Machine learning (ML) techniques can bring a large contribute on the process of prediction and early diagnosis of breast cancer, became a research hotspot and has been proved as a strong technique. Using ML models performed on multidimensional dataset, this article aims to find the most efficient and accurate ML models for tumor classification prediction.

Several supervised ML algorithms were utilized to diagnosis and prediction of cancer tumor such as Logistic Regression Decision Tree, Random Forest and KNN. The algorithms are applied to a dataset taken from the UCI repository including 699 samples. The dataset includes Breast cancer features. To enhance the algorithms’ performance, these features are analyzed, the feature importance score and cross validation are considered. In this research, ML algorithms improved coupled by limited and selective features to produce high classification accuracy in tumor classification.

As a result of evaluation, Logistic Regression algorithm with accuracy value equal to 99.14%, AUC ROC equal to 99.6%, Extra Tree algorithm with accuracy value equal to 99.14% and AUC ROC equal to 99.1% have better performance than other algorithms. Therefore, these techniques can be useful for diagnosis and prediction of cancer tumor and prescribe it correctly.

The technique of ML can be used in medicine for analyzing the related data collections to a disease and its prediction. The area under the ROC curve and evaluating criteria related to a number of classifying algorithms of ML to evaluate breast cancer and indeed, the diagnosis and prediction of breast cancer is compared to determine the most appropriate classifier.

Estimating the probability of obstructive coronary artery disease in patients undergoing noncoronary cardiac surgery should be considered compulsory. Our study sought to evaluate the prevalence of obstructive coronary artery disease in patients undergoing valvular heart surgery and to utilize predictive methodology of concomitant obstructive coronary artery disease in these patients.

The retrospective study cohort was derived from a tertiary care hospital registry of patients undergoing coronary angiogram prior to valvular heart operations. Decision tree, logistic regression, and support vector machine models were built to predict the probability of the appearance of obstructive coronary artery disease. A total of 367 patients from 2016 to 2019 were analyzed.

The mean age of the study population was 57.3±9.3 years, 45.2% of the patients were male. Of 367 patients, 76 (21%) patients had obstructive coronary artery disease. The decision tree, logistics regression, and support vector machine models had an area under the curve of 72% (95% CI: 62% - 81%), 67% (95% CI: 56% - 77%), and 78% (95% CI: 68% - 87%), respectively. Multivariate analysis indicated that hypertension (OR 1.98; P=0.032), diabetes (OR 2.32; P=0.040), age (OR 1.05; P=0.006), and typical angina (OR 5.46; P<0.001) had significant role in predicting the presence of obstructive coronary artery disease.

Our study revealed that approximately one-fifth of patients who underwent valvular heart surgery had concomitant obstructive coronary artery disease. The support vector machine model showed the highest accuracy compared to the other model.

We aimed to evaluate the possible role of the age, occlusion type, type of dentition (full dentition or free-end extensions), and type of temporomandibular disorders (TMD) to predict the presence of pain.

Subjects were selected from volunteer male TMD patients with one partially edentulous jaw from the Baghdad city in 2022. Pain was assessed via the Visual Analogue Scale (VAS). Angle’s and Kennedy’s classifications were employed to assess occlusion and partially edentulous jaw conditions. TMD was assessed using Diagnostic Criteria for Temporomandibular Disorders (DC/TMD) for Clinical and Research Applications. Relationship between pain as ordinal dependent variable and other predictor variables was assessed via ordinal logistic regression using SPSS 26.

240 subjects were assessed for eligibility and 180 TMD patients (mean age 41.1 ± 0.46) were included in the study. The omnibus test showed that the model outperforms the null model (p < 0.001). Disc displacement with reduction (odds ratio: 0.09) and Kennedy’s Class I (odds ratio: 0.42) were statistically significant inverse predictors for pain (p < 0.05). Age (p= 0.66) and Angle’s occlusion type (p= 0.91) were not significant predictors for pain.

Probability of pain decreased in disc displacement with reduction and Kennedy’s Class I.