Application of data mining techniques for predicting residents' performance on pre-board examinations: A case study

Developing the breast cancer risk prediction system using hybrid machine learning algorithms

BACKGROUND

Breast cancer (BC) is the most common cause of cancer-related deaths in women globally. Currently, many machine learning (ML)-based predictive models have been established to assist clinicians in decision making for the prediction of BC. However, preventing risk factor formation even with having healthy lifestyle behaviors or preventing disease at early stages can significantly lead to optimal population-wide BC health. Thus, we aimed to develop a prediction model by using a genetic algorithm (GA) incorporating several ML algorithms for the prediction and early warning of BC.

MATERIAL AND METHODS

The data of 3168 healthy individuals and 1742 patient case records in the BC Registry Database in Ayatollah Taleghani hospital, Abadan, Iran were analyzed. First, a modified hybrid GA was used to perform feature selection and optimization of selected features. Then, with the use of selected features, several ML algorithms were trained to predict BC. Afterward, the performance of each model was measured in terms of accuracy, precision, sensitivity, specificity, and receiver operating characteristic (ROC) curve metrics. Finally, a clinical decision support system based on the best model was developed.

RESULTS

After performing feature selection, age, consumption of dairy products, BC family history, breast biopsy, chest X-ray, hormone therapy, alcohol consumption, being overweight, having children, and education statuses were selected as the most important features for prediction of BC. The experimental results showed that the decision tree yielded a superior performance than other ML models, with values of 99.3%, 99.5%, 98.26% for accuracy, specificity, and sensitivity, respectively.

CONCLUSION

The developed predictive system can accurately identify persons who are at elevated risk for BC and can be used as an essential clinical screening tool for the early prevention of BC and serve as an important tool for developing preventive health strategies.

Explaining Internal Medicine and Surgery Residents' Perceptions of Mentor as a Role Model: A qualitative Study

Background: Role modeling is the essence of the teaching process and one of the important functions of educators and clinical instructors. In clinical education situations, many clinical instructors are responsible as mentors. On the other hand, clinical instructors, although not obvious, are seen as role models. This study is aimed at explaining the internal medicine and surgery residents' perceptions of mentors as role models. Methods: This qualitative (content analysis) study was carried out using purposive sampling and conducting semi-structured interviews with 18 medical residents (internal medicine and surgery departments) at Iran University of Medical Sciences. The resulting data were analyzed using inductive qualitative content analysis. Results: Based on the results of the analysis of the transcribed interviews, 60 initial codes, 31 subthemes, and 5 main themes were identified. The dimensions of the mentor's role modeling were as follows: structural, executive, managerial, ethical, and scientific. Conclusion: Role modeling is a very effective learning method, especially for medical students. Role modeling in clinical learning environments seems to be valuable to facilitate students' learning. Using the role of clinical instructors as an educational model is one of the important ways of teaching professional ethics in clinics.

Using logistic regression to develop a diagnostic model for COVID-19: A single-center study

BACKGROUND

The main manifestations of coronavirus disease-2019 (COVID-19) are similar to the many other respiratory diseases. In addition, the existence of numerous uncertainties in the prognosis of this condition has multiplied the need to establish a valid and accurate prediction model. This study aimed to develop a diagnostic model based on logistic regression to enhance the diagnostic accuracy of COVID-19.

MATERIALS AND METHODS

A standardized diagnostic model was developed on data of 400 patients who were referred to Ayatollah Talleghani Hospital, Abadan, Iran, for the COVID-19 diagnosis. We used the Chi-square correlation coefficient for feature selection, and logistic regression in SPSS V25 software to model the relationship between each of the clinical features. Potentially diagnostic determinants extracted from the patient's history, physical examination, and laboratory and imaging testing were entered in a logistic regression analysis. The discriminative ability of the model was expressed as sensitivity, specificity, accuracy, and area under the curve, respectively.

RESULTS

After determining the correlation of each diagnostic regressor with COVID-19 using the Chi-square method, the 15 important regressors were obtained at the level of P < 0.05. The experimental results demonstrated that the binary logistic regression model yielded specificity, sensitivity, and accuracy of 97.3%, 98.8%, and 98.2%, respectively.

CONCLUSION

The destructive effects of the COVID-19 outbreak and the shortage of healthcare resources in fighting against this pandemic require increasing attention to using the Clinical Decision Support Systems equipped with supervised learning classification algorithms such as logistic regression.