Comparing Performance of Data Mining Algorithms in Prediction Heart Diseases

Heart diseases are among the nation’s leading couse of mortality and moribidity. Data mining teqniques can predict the likelihood of patients getting a heart disease. The purpose of this study is comparison of different data mining algorithm on prediction of heart diseases. This work applied and compared data mining techniques to predict the risk of heart diseases. After feature analysis, models by five algorithms including decision tree (C5.0), neural network, support vector machine (SVM), logistic regression and k-nearest neighborhood (KNN) were developed and validated. C5.0 Decision tree has been able to build a model with greatest accuracy 93.02%, KNN, SVM, Neural network have been 88.37%, 86.05% and 80.23% respectively. Produced results of decision tree can be simply interpretable and applicable; their rules can be understood easily by different clinical practitioner

Electronic Health Record Implementation: A SWOT Analysis

Electronic Health Record (EHR) is one of the most important achievements of information technology in healthcare domain, and if deployed effectively, it can yield predominant results. The aim of this study was a SWOT (strengths, weaknesses, opportunities, and threats) analysis in electronic health record implementation. This is a descriptive, analytical study conducted with the participation of a 90-member work force from Hospitals affiliated to Tehran University of Medical Sciences (TUMS). The data were collected by using a self-structured questionnaire and analyzed by SPSS software. Based on the results, the highest priority in strength analysis was related to timely and quick access to information. However, lack of hardware and infrastructures was the most important weakness. Having the potential to share information between different sectors and access to a variety of health statistics was the significant opportunity of EHR. Finally, the most substantial threats were the lack of strategic planning in the field of electronic health records together with physicians’ and other clinical staff’s resistance in the use of electronic health records. To facilitate successful adoption of electronic health record, some organizational, technical and resource elements contribute; moreover, the consideration of these factors is essential for HER implementation

Investigating the Effectiveness of Mobile Phone-based Education on Learning Pain Management Skills in Nursing Student

Introduction: Nurses are the first line in assessing the patient's pain and performing palliative measures, and their proper training is essential. The aim of this study was to compare the effectiveness of lecture and mobile phone-based teaching methods on the knowledge and performance of nursing students in patient’s pain management. Methods: The present study was a semi-experimental before and after study with two intervention and control groups. The samples included 52 nursing students in Saveh Medical Sciences Faculty. Learners were placed in two intervention (mobile based application) and control (lecture) groups by a simple random method. The data collection tool included a questionnaire to measure the level of awareness and a checklist to measure the pain management skills of patients by the learners. Mann-Whitney, paired t test and independent t test were used to data analysis. Result: After training, students' knowledge scores about pain management increased significantly in both intervention and control groups. In addition, there was a significant difference in the mean score of pain management skill and its measurement among the learners of both groups after the intervention (P<0.05). Conclusion: The use of a mobile-based application can improve pain management and pain measurement skills in nursing students. The correct use of these programs can improve the quality of students' learning and also improve their knowledge and skills

An investigation of data mining techniques of the performance of a decision tree algorithm for predicting causes of traumatic brain injuries in Khatamolanbya Hospital in Zahdan city, 2012 to 2013

Introduction: The aim of this study was to determine the performance of data mining techniques for predicting the causes of traumatic brain injuries in Khatamolanbya hospital, Zahdan city. Method: In this cross–sectional, the study population included all patients who died of brain injury. Data were collected by the use of a researcher- made check list, provided under the direct observation of authorities in this area and analyzed by the data mining software of Clementine 12.0. Results: According to the results of this algorithm, C5.0 decision tree algorithm has an accuracy of 81.4 percent, the highest precision; then, the algorithm is C & R(The Classification and Regression) with 77.8 percent. Conclusion: Overall, it can be concluded from the decision tree algorithm that age is one of the leading causes of traumatic brain injuries . The results showed that all the cases involving traumatic lesions of the brain lead to the patient’s death.. Although in some algorithms, some of the variables are important, they cannot be used alone as the main variable to be taken into account for the death of the patient