Predicting the Need for Cardiovascular Surgery: A Comparative Study of Machine Learning Models

This research examines the efficacy of ensemble Machine Learning (ML) models, mainly focusing on Deep Neural Networks (DNNs), in predicting the need for cardiovascular surgery, a critical aspect of clinical decision-making. It addresses key challenges such as class imbalance, which is pivotal in healthcare settings. The research involved a comprehensive comparison and evaluation of the performance of previously published ML methods against a new Deep Learning (DL) model. This comparison utilized a dataset encompassing 50,000 patient records from a large hospital between 2015-2022. The study proposes enhancing the efficacy of these models through feature selection and hyperparameter optimization, employing techniques like grid search. A novel aspect of this research was the comparison of a newly developed DNN model with existing ensemble models based on similar cardiovascular datasets. The results indicated the DNN model\u27s superior predictive accuracy, demonstrating an Area Under the Curve (AUC) of 74%, alongside notable precision (68%) and recall (72%) for the minority class, which indicates patients requiring surgery. The model further achieved a 70% F1-Score and a balanced accuracy rate of 72%, significantly outperforming the existing ensemble models in every key performance metric. The study underscores the transformative potential of DNNs in predictive modeling for cardiovascular care and highlights the importance of integrating advanced ML techniques into clinical workflows. Future research should delve into the practical application and integration of these models

The Effective Factors on Success of E-learning in Medical Sciences Fields

Introduction: Today, the use of information technology in education, is one of the most important goals of many educational institutions around the world and Iran, due to its advantages including the possibility to be used at any time and place. ACECR branch of Tehran University of Medical Sciences, as one of the organizers of specialized training courses, has already organized some e-learning courses. In this research, we tried to examine the role of each of the effective factors in e-learning in the success rate of this type of education. Methods: In this descriptive study, the effect of six factors, from the perspective of the four groups of participants, professors and ACECR (Iranian academic center for education, culture and research) staff and managers, was measured in 2010-2011, through using a researcher-made questionnaire including 39 questions on Likert scale. The questionnaire was sent via e-mail to 420 people and a total of 188 people participated in this study. The collected data were analyzed through SPSS software and using descriptive statistics and inferential statistics. Results: The results indicated the importance of all studied factors with the order of management, educational content, facilities, teachers, rules and learner. Conclusion: Although the difference between the effective factors is negligible, the extra effort of authorities to remove the shortcomings and weaknesses of the three main and effective factors of management, educational content and facilities needed for the success of e-earning is required

CREATING A SEMI-SUPERVISED DATA MINING MODEL FOR PREDICTING BREAST CANCER RELAPSE

Introduction: Breast cancer is one of the most common types of cancer and malignancy in Iranian women,that recently has been a growing increase. there is always a possibility of recurrence In persons afflicted by thisdisease.In regarding to the complexity of analysis, data mining is among the best solutions that is used to detect or predict cancers. Methods: In this retrospective study, data of 809 patients with breast cancer from center of breast cancer research of Tehran’s Academic Center for Education, Culture and Research (ACECR) and 26 features from each patients were used. In regarding to high number of missing data in this collection, only information of 655 patients and 14 features of each patient were usable. Many features in records have null values, thus as one of data pre-processing and preparing phases, via Auto-Clustering algorithm, the data was divided into 10 clusters and according to dominant values in each cluster for each feature, these null features has been valuing. Data was divided to recurrence and non- recurrence classes. Semi-supervised method has been used in this study. the modeling was performed using labeled data and then a hybrid model for giving label to nonlabled data has been created. For this, data with recurrence lable divided proportional 30 percent for testing and 70 percent for training,and got to decision tree algorithms C5.0, Chaid, Quest, CRT, Autoclassifier as inputs. Then, the model was formed by mixing classifier algorithms with Confidence-Weighting-Voting method for predicting cancer recurrence, and used K-Fold (K=10) method for evaluating created model. Results: The sensitivity of developed hybrid model was 82.93% and its specificity was 93.93%. The precision value of the model is 89.47% and its accuracy is 89.72%. this model mistakenly labelled only 10% of recurrence in patients of breast cancer as non-recurrence. Conclusion: Creating predictive models with an appropriate sensitivity and specificity isimportant since, if the possibility of recurrence is high, could perform special preventive proceedings before its spreading. The false negative percentage is also important in medical prediction models, as it can have very dangerous consequences. In the prediction model presented in this study, the value of this parameter was 10%, and in this regard, this model can be considered acceptabl

A Prognostic Model Based on Data Mining Techniques to Predict Breast Cancer Recurrence

Introduction: Breast cancer is one of the most common cancers, and also it is the most common type of malignancy in Iranian women that has been growing in recent years. The risk of recurrence is usual in patient. Many factors may increase or decrease the recurrence rate. Data mining methods have been used to diagnose or predict cancer and one of the most application of data mining approaches is prediction of breast cancer recurrence Method: This is a retrospective study. Collected data on 809 patients with breast cancer with 18 fields for each patient were used. Due to excessive missing data only about 665 cases have been used. Since the number of fields in the remaining records with null values have been observed, as a preprocessing and data preparation phases, these values have been estimated by the EM algorithm and using SPSS.v20 software. In this study, a model for prognosis of breast cancer recurrence among patients using J48 tree has been developed. Results: The specificity and sensitivity of the developed model are 53% and 85%, respectively. Moreover, only 14% of patients who have relapsed are known as false negative with developed model. Conclusion: Creating a predictive model with appropriate specificity and sensitivity can warn patients about recurrence and timely preventive measures to prevent progression of the cancer. The False Negative rate is very important in medical prediction models that can make serious results/consequences. In present study this rate is about 14% that seems reasonable amount in term of modeling

User Interface Problems of a Nationwide Inpatient Information System: A Heuristic Evaluation

Introduction: While studies have shown that usability evaluation could uncover many design problems of health information systems, the usability of health information systems in developing countries using their native language is poorly studied. The objective of this study was to evaluate the usability of a nationwide inpatient information system used in many academic hospitals in Iran. Material and Methods: Three trained usability evaluators independently evaluated the system using Nielsen’s 10 usability heuristics. The evaluators combined identified problems in a single list and independently rated the severity of the problems. We statistically compared the number and severity of problems identified by HIS experienced and non-experienced evaluators. Results: A total of 158 usability problems were identified. After removing duplications 99 unique problems were left. The highest mismatch with usability principles was related to “Consistency and standards” heuristic (25%) and the lowest related to “Flexibility and efficiency of use” (4%). The average severity of problems ranged from 2.4 (Major problem) to 3.3 (Catastrophe problem). The experienced evaluator with HIS identified significantly more problems and gave higher severities to problems (p<0.02). Discussion: Heuristic Evaluation identified a high number of usability problems in a widely used inpatient information system in many academic hospitals. These problems, if remain unsolved, may waste users’ and patients’ time, increase errors and finally threaten patient’s safety. Many of them can be fixed with simple redesign solutions such as using clear labels and better layouts. This study suggests conducting further studies to confirm the findings concerning effect of evaluator experience on the results of Heuristic Evaluation

Caracterização de bio-óleos obtidos por pirólise da serragem de Eucalyptus sp. (hardwood) e picea abies (softwood) utilizando as técnicas de cromatografia gasosa acoplada à espectrometria de massas

Bio-óleos obtidos através de pirólise de biomassas lignocelulósicas são uma alternativa complementar às fontes fósseis no processo de fabricação de combustíveis e outros produtos químicos. Foi feita uma comparação entre os bio-óleos obtidos em reatores de leito fixo (FB) e leito fluidizado borbulhante (BFB), empregando-se serragem de Eucalyptus sp (hardwood) e de Picea abies (softwood), resíduos produzidos em larga escala em diversos países. Observou-se maior rendimento do produto líquido (bio-óleo bruto) da pirólise em reator BFB (~70 %) do que no reator FB (~50 %). As cetonas e os fenóis foram os compostos majoritários obtidos nos bio-óleos, respectivamente. A predominância destes compostos químicos sugere que estes bio-óleos apresentam potencial para a indústria de polímeros, alimentícia entre outras. A análise realizada por GC×GC/TOFMS se mostrou importante para o estudo de três bio-óleos obtidos a partir de BFB, visto que foram verificadas imprecisões na análise dos mesmos bio-óleos quando a 1D-GCqMS foi utilizada, devido a co-eluições. O emprego de zeólita ZSM-5 em reator BFB aumentou o percentual de hidrocarbonetos aromáticos no bio-óleo, mostrando o potencial deste tipo de processo e resíduo para produção de combustível e a presença de hidrocarbonetos poliaromáticos trouxe um alerta para o correto gerenciamento da pirólise a fim de evitar a produção de compostos tóxicos.Bio-oils obtained by pyrolysis of lignocellulosic biomass are a complementary alternative to fossil fuels in the manufacturing process of fuels and other chemicals. A comparison was made between the bio-oils obtained in fixed bed (FB) and bubbling fluidized bed (BFB) reactors, using sawdust of Eucalyptus sp. (hardwood) and Picea abies (softwood), waste produced on a large scale in different countries. It was observed a higher yield of liquid product in BFB reactor (~ 70%) than in the FB reactor (~ 50%). The ketones and phenols were the major compounds obtained in these bio-oils, respectively. The prevalence of these chemical compounds suggests that these bio-oils have potential for the polymer industry, food and others. The analysis by GC×GC/TOFMS was important for the study of the three bio-oils obtained from BFB, as inaccuracies in the 1D-GCqMS analysis were verified due to co-elutions. The use of zeolite ZSM-5 as a catalyst on the BFB reactor increased the percentage of aromatic hydrocarbons in the bio-oil, showing the potential of this type of process and residue for fuel production and the presence of polyaromatic hydrocarbons brought an alert to the proper management of pyrolysis in order to avoid the production of toxic compounds

Predicting the mortality of patients with Covid‐19: A machine learning approach

Abstract Background and Aims Infection with Covid‐19 disease can lead to mortality in a short time. Early prediction of the mortality during an epidemic disease can save patients' lives through taking timely and necessary care interventions. Therefore, predicting the mortality of patients with Covid‐19 using machine learning techniques can be effective in reducing mortality rate in Covid‐19. The aim of this study is to compare four machine‐learning algorithm for predicting mortality in Covid‐19 disease. Methods The data of this study were collected from hospitalized patients with COVID‐19 in five hospitals settings in Tehran (Iran). Database contained 4120 records, about 25% of which belonged to patients who died due to Covid‐19. Each record contained 38 variables. Four machine‐learning techniques, including random forest (RF), regression logistic (RL), gradient boosting tree (GBT), and support vector machine (SVM) were used in modeling. Results GBT model presented higher performance compared to other models (accuracy 70%, sensitivity 77%, specificity 69%, and the ROC area under the curve 0.857). RF, RL, and SVM models with the ROC area under curve 0.836, 0.818, and 0.794 were in the second and third places. Conclusion Considering the combination of multiple influential factors affecting death Covid‐19 can help in early prediction and providing a better care plan. In addition, using different modeling on data can be useful for physician in providing appropriate care

Prevalence, Risk Factors and Consequent Effect of Dystocia in Holstein Dairy Cows in Iran

The objective of this research was to determine the prevalence, risk factors and consequent effect of dystocia on lactation performance in Holstein dairy cows in Iran. The data set consisted of 55,577 calving records on 30,879 Holstein cows in 30 dairy herds for the period March 2000 to April 2009. Factors affecting dystocia were analyzed using multivariable logistic regression models through the maximum likelihood method in the GENMOD procedure. The effect of dystocia on lactation performance and factors affecting calf birth weight were analyzed using mixed linear model in the MIXED procedure. The average incidence of dystocia was 10.8% and the mean (SD) calf birth weight was 42.13 (5.42) kg. Primiparous cows had calves with lower body weight and were more likely to require assistance at parturition (p<0.05). Female calves had lower body weight, and had a lower odds ratio for dystocia than male calves (p<0.05). Twins had lower birth weight, and had a higher odds ratio for dystocia than singletons (p<0.05). Cows which gave birth to a calf with higher weight at birth experienced more calving difficulty (OR (95% CI) = 1.1(1.08–1.11). Total 305-d milk, fat and protein yield was 135 (23), 3.16 (0.80) and 6.52 (1.01) kg less, in cows that experienced dystocia at calving compared with those that did not (p<0.05)

Bone Metastasis Prognostic Factors in Breast Cancer

Objective: Bone is the most common site of metastasis in breast cancer. Prognostic factors for predicting bone metastases in breast cancer are controversial yet. In this study, we investigated clinical factors associated with secondary bone metastasis of breast cancer. Methods: In total, 1690 patients with breast cancer recorded between 2002 and 2012 in Motamed Cancer Institute, Tehran, Iran entered in the retrospective study. We studied age, menopausal status, histologic type, tumor size, number of cancerous axillary lymph nodes, serum concentrations of alkaline phosphatase (ALP), carcinogenicity antigen (CEA), cancer antigen (CA)-153, and hemoglobin (HB) in 2 groups with bone metastases (n = 123) and without it, respectively. We applied logistic regression to identify bone metastasis prognostic factors in breast cancer patients and calculated the cut-off value, sensitivity, and characteristics of independent prognostic factors using receiver operating characteristic (ROC) curve analysis. Results: Menopause, larger tumor size, and the greater number of cancerous axillary lymph nodes increased the chance of bone metastases significantly ( P   .05). Logistic regression showed that age (odds ratio (OR) = 1.021), menopausal status (OR = 1.854), number of cancerous axillary lymph nodes (OR = 1.065), a tumor size between 2 and 5 cm diameter (OR = 2.002) and more than 5 cm diameter (OR = 4.009), and ALP (OR = 1.005) are independent prognostic factors associated with bone metastases. The ROC curve showed that the abovementioned factors have comparable predictive accuracy for bone metastases. Conclusions: Age, menopausal status, number of axillary lymph node metastases, tumor size, and ALP were identified as prognostic factors for bone metastasis in patients with breast cancer. So patients with these characteristics should be monitored more precisely with regular follow-ups