AFEX-Net: Adaptive feature extraction convolutional neural network for classifying computerized tomography images.

Purpose: Deep convolutional neural networks are favored methods that are widely used in medical image processing due to their demonstrated performance in this area. Recently, the emergence of new lung diseases, such as COVID-19, and the possibility of early detection of their symptoms from chest computerized tomography images has attracted many researchers to classify diseases by training deep convolutional neural networks on lung computerized tomography images. The trained networks are expected to distinguish between different lung indications in various diseases, especially at the early stages. The purpose of this study is to introduce and assess an efficient deep convolutional neural network, called AFEX-Net, that can classify different lung diseases from chest computerized tomography images. Methods: We designed a lightweight convolutional neural network called AFEX-Net with adaptive feature extraction layers, adaptive pooling layers, and adaptive activation functions. We trained and tested AFEX-Net on a dataset of more than 10,000 chest computerized tomography slices from different lung diseases (CC dataset), using an effective pre-processing method to remove bias. We also applied AFEX-Net to the public COVID-CTset dataset to assess its generalizability. The study was mainly conducted based on data collected over approximately six months during the pandemic outbreak in Afzalipour Hospital, Iran, which is the largest hospital in Southeast Iran. Results: AFEX-Net achieved high accuracy and fast training on both datasets, outperforming several state-of-the-art convolutional neural networks. It has an accuracy of 99.7% and 98.8% on the CC and COVID-CTset datasets, respectively, with a learning speed that is 3 times faster compared to similar methods due to its lightweight structure. AFEX-Net was able to extract distinguishing features and classify chest computerized tomography images, especially at the early stages of lung diseases. Conclusion: The AFEX-Net is a high-performing convolutional neural network for classifying lung diseases from chest CT images. It is efficient, adaptable, and compatible with input data, making it a reliable tool for early detection and diagnosis of lung diseases.

The viewpoints of parents of children with mental disorders regarding the confidentiality and security of their children's information in the Iranian national electronic health record system.

INTRODUCTION: Electronic health records help collect and communicate patient information among healthcare providers. The confidentiality of information, especially for patients with mental disorders, is paramount due to its profound impacts on individuals' lives' social and personal aspects. This study aimed to investigate the viewpoints and concerns of parents of children with mental disorders regarding the confidentiality and security of their children's information in the Iranian National Electronic Health Record System (IEHRS). METHODS: This is a survey study on parents or guardians of children with mental disorders who visited Kerman's specialised child psychiatry treatment centres. The data collection tool was a researcher-made questionnaire with 28 questions organised in seven sections, including demographic information of parents, children's medical history, Internet use, knowledge about IEHRS, the necessity of data collection, IEHRS security concerns, and privacy concerns. The data were analysed in SPSS 24 software using descriptive statistics and logistic and ordinal regressions to assess the relationship between parents' demographic characteristics and their viewpoints regarding information security and confidentiality concerns. RESULTS: The results showed that more than 85 % of the parents believed that the security of their children's information in IEHRS was moderate to high. More than two-thirds (71 %) of the parents also believed that IEHRS should tighten its privacy policies. Most participants (87 %) were concerned about their children's information security in IEHRS. In this study, the parents' concerns about the privacy and security of information in IEHRS were not significantly associated with their age, gender, or knowledge about IEHRS. CONCLUSIONS: Most parents of children with mental disorders were concerned about the security and confidentiality of their children's information in IEHRS. Thus, health policymakers should maintain a high level of security and establish appropriate privacy and confidentiality rules in IEHRS. In addition, they should be transparent about the system's security mechanisms and confidentiality regulations to win public trust.

The relationship between e-health literacy and information technology acceptance, and the willingness to share personal and health information among pregnant women.

BACKGROUND: Many factors may affect pregnant women's willingness to accept information (IT) technology and share their personal and health information. One of these factors is their e-health literacy level. OBJECTIVE: To investigate the relationship between e-health literacy and IT acceptance, as well as the willingness of pregnant women to share their information. METHODS: This survey was conducted among pregnant women visiting hospitals and private physicians' offices in Zahedan, Iran in 2019. Data were collected using a 4-part questionnaire with 66 questions. The data were analyzed using descriptive (frequency, percentage, mean and standard deviation) and inferential (Pearson correlation coefficient and linear regression) statistics. RESULTS: The mean scores of electronic health literacy, information technology acceptance, and willingness of pregnant women to share personal and health information were 27.43 ± 5.82, 145.49 ± 25.72, and 19.16 ± 5.47, respectively. There was a significant relationship between IT acceptance and information sharing, which means that with increasing IT acceptance, people were more willing to share their information. Also, the results showed that with the decrease in economic well-being, the willingness to share personal and health information decreases. CONCLUSION: This study showed that with the increase in e-health literacy of pregnant women, their IT acceptance grows. Increasing IT acceptance improves their willingness to share their information. Setting and updating information-sharing rules and security mechanisms with the participation of people can help reduce concerns and increase public trust. Healthcare policymakers can encourage the use of health IT in the prevention and treatment of diseases by providing relevant education and informing people.

A systematic review of data elements of computerized physician order entry (CPOE): mapping the data to FHIR.

OBJECTIVE: Medication errors are the third leading cause of death. There are several methods to prevent prescription errors, one of which is to use a Computerized Physician Order Entry system (CPOE). In a CPOE system, necessary data needs to be collected so that making decisions about prescribing medications and treatment plans could be made. Although many CPOE systems have been developed worldwide, studies have yet to identify the necessary data and data elements of CPOE systems. This study aims to identify data elements of CPOE and standardize these data with Fast Healthcare Interoperability Resources (FHIR) to facilitate data sharing and integration with the electronic health record (EHR) system and reduce data diversity. METHODS: PubMed, Web of Science, Embase, and Scopus databases for studies up to October 2019 were searched. Two reviewers independently assessed original articles to determine eligibility for inclusion in this review. All articles describing data elements of a COPE system were included. Data elements were obtained from the included articles' text, tables, and figures.Classification of the extracted data elements and mapping them to FHIR was done to facilitate data sharing and integration with the electronic health record (EHR) system and reduce data diversity. The final data elements of CPOE were categorized into five main categories of FHIR (foundation, base, clinical, financial, and specialized) and 146 resources, where possible. One of the researchers did mapping and checked and verified by the second researcher. If a data element could not be mapped to any FHIR resources, this data element was considered an extension to the most relevant resource. RESULTS: We retrieved 5162 articles through database searches. After the full-text assessment, 21 articles were included. In total, 270 data elements were identified and mapped to the FHIR standard. These elements have been reported in 26 FHIR resources of 146 ones (18%). In total, 71 data elements were considered an extension. CONCLUSIONS: The results of this study showed that the same data elements were not used in the CPOE systems, and the degree of homogeneity of these systems is limited. The mapping of extracted data with data elements used in the FHIR standard shows the extent to which these systems comply with existing standards. Considering the standards in these systems' design helps developers design more coherent systems that can share data with other systems.

Prediction of mortality risk and duration of hospitalization of COVID-19 patients with chronic comorbidities based on machine learning algorithms.

Background: The severity of coronavirus (COVID-19) in patients with chronic comorbidities is much higher than in other patients, which can lead to their death. Machine learning (ML) algorithms as a potential solution for rapid and early clinical evaluation of the severity of the disease can help in allocating and prioritizing resources to reduce mortality. Objective: The objective of this study was to predict the mortality risk and length of stay (LoS) of patients with COVID-19 and history of chronic comorbidities using ML algorithms. Methods: This retrospective study was conducted by reviewing the medical records of COVID-19 patients with a history of chronic comorbidities from March 2020 to January 2021 in Afzalipour Hospital in Kerman, Iran. The outcome of patients, hospitalization was recorded as discharge or death. The filtering technique used to score the features and well-known ML algorithms were applied to predict the risk of mortality and LoS of patients. Ensemble Learning methods is also used. To evaluate the performance of the models, different measures including F1, precision, recall, and accuracy were calculated. The TRIPOD guideline assessed transparent reporting. Results: This study was performed on 1291 patients, including 900 alive and 391 dead patients. Shortness of breath (53.6%), fever (30.1%), and cough (25.3%) were the three most common symptoms in patients. Diabetes mellitus(DM) (31.3%), hypertension (HTN) (27.3%), and ischemic heart disease (IHD) (14.2%) were the three most common chronic comorbidities of patients. Twenty-six important factors were extracted from each patient's record. Gradient boosting model with 84.15% accuracy was the best model for predicting mortality risk and multilayer perceptron (MLP) with rectified linear unit function (MSE = 38.96) was the best model for predicting the LoS. The most common chronic comorbidities among these patients were DM (31.3%), HTN (27.3%), and IHD (14.2%). The most important factors in predicting the risk of mortality were hyperlipidemia, diabetes, asthma, and cancer, and in predicting LoS was shortness of breath. Conclusion: The results of this study showed that the use of ML algorithms can be a good tool to predict the risk of mortality and LoS of patients with COVID-19 and chronic comorbidities based on physiological conditions, symptoms, and demographic information of patients. The Gradient boosting and MLP algorithms can quickly identify patients at risk of death or long-term hospitalization and notify physicians to do appropriate interventions.

The role of mobile health in prevention, diagnosis, treatment and self-care of COVID-19 from the healthcare professionals' perspectives.

Background: To facilitate disease management, understanding the attitude of healthcare professionals regarding the use of this tool can help mobile health (mHealth) program developers develop appropriate interventions. Aims: To assess the perspective of healthcare professionals regarding the contribution of mobile-based interventions in the prevention, diagnosis, self-care, and treatment (PDST) of COVID-19. Methods: This is a survey study conducted in 2020 in Iran with 81 questions. In this study mHealth functionalities were categorized into four dimensions including innovative, monitoring and screening, remote services, and education and decision-making. The data were analyzed using descriptive statistics, ANOVA, and the Kruskal-Wallis test to compare the attitudes of the different job groups. Results: In total, 123 providers participated, and 87.4% of them reported that mHealth technology is moderate to most helpful for the management of COVID-19. Healthcare professionals believed that mHealth technology could be most helpful in self-care and least helpful in the diagnosis of COVID-19. Regarding the functionalities of the mobile application, the results showed that the use of patient decision aids can be most helpful in self-care and the use of computer games can be least helpful in treatment. The participants believed that mHealth is more effective in monitoring and screening dimensions and less effective in providing remote services. Conclusions: This study showed that healthcare professionals believed that mHealth technology could have a better contribution to self-care for patients with COVID-19. Therefore, it is better to plan and invest more in the field of self-care to help patients to combat COVID-19. The results of this study revealed which mhealth functionalities work better in four domains of prevention, treatment, self-care, and diagnosis of COVID-19. This can help healthcare authorities to implement appropriate IT-based interventions to combat COVID-19.

Developing a minimum data set for cardiovascular Computerized Physician Order Entry (CPOE) and mapping the data set to FHIR: A multi-method approach.

Many medical errors occur in the process of treating cardiovascular patients, and most of these errors are related to prescription errors. There are several, one of the methods to prevent prescription errors is the use of a computerized physician order entry (CPOE) system. One of the obstacles of implementing this system is improper design and non-compliance with user needs. one of the issues that should be considered in designing information systems is having a standard minimum data set (MDS). Although many computerized physicians order entry (CPOE) systems have been developed in the world, no study has identified the necessary data and minimum data set (MDS) of CPOE system, and published the process of creating this MDS. This study aimed to develop an MDS for cardiovascular CPOE and standardize it with Fast Healthcare Interoperability Resources (FHIR). A multi-method approach including systematic review for identifying data elements of CPOE, reviewing the content of medical records, validation of the data elements using the expert panel and, determination of the necessary data elements using a survey was conducted. Classification of the data elements and mapping them to FHIR were done to facilitate data sharing and integration with the electronic health record (EHR) system as well as to reduce data diversity. The final data elements of MDS were categorized into 5 main categories of FHIR (foundation, base, clinical, financial, and specialized) and 146 resources, where possible. Mapping was done by one of the researchers and checked and verified by the second researcher. Non-mapped data elements were added to relevant resources as extensions of existing FHIR resources. In total, 270 data elements were identified from the systematic review. After reviewing the content of 20 patients' medical records, 28 data elements were identified. After combination of data elements of two previous phases and removing duplication, 282 data elements remained. Data elements that were considered necessary to be included in CPOE by conducting a survey among cardiovascular physicians were 109 elements. From 146 resources of FHIR, the data elements of this MDS are covered by 5 resources. This study introduced an MDS for cardiovascular CPOE by combining suggested data elements of previous research, and the practical and local requirements identified in patients' medical records. To facilitate data sharing and integration with EHR, reduce data diversity, and also to categorize data, this MDS was standardized with FHIR. The steps we used to develop this MDS could be a model for creating MDS in other CPOEs and health information systems. This is the first time that the process of developing an MDS for cardiovascular CPOE has been presented in the literature.

A Rule Based Intelligent Software to Predict Length of Stay and the Mortality Rate in Trauma Patients in the Intensive Care Unit.

Background: Intensive Care Unit (ICU) has the highest mortality rate in the world. ICU has special equipment that leads to the hospital's most costly parts. The length of stay in the ICU is a special issue, and reducing this time is a practical approach. We aimed to use artificial intelligence to help early and timely diagnosis of the disease to help with health. Methods: We designed a rule-based intelligent system to predict the length of stay and the mortality rate of trauma patients in ICU. A neuro-Fuzzy and eight machine learning models were used to predict the mortality rate in trauma patients in ICU. The performances of these techniques were evaluated with accuracy, sensitivity, specificity, and area under the ROC curve. Decision-Table was used to predict the length of stay in trauma patients in ICU. For comparison, eight machine learning models were used. The method is compared based on Mean absolute error and relative absolute error (%). Results: Neuro-Fuzzy expert system and Decision-Table showed better results than other techniques. Accuracy, sensitivity, specificity, and ROC Area of Nero-Fuzzy are 83.6735, 0.9744, 0.3000, 0.8379, and 1, respectively. The mean absolute error and Relative absolute error (%) of the Decision-Table model are 4.5426 and 65.4391, respectively. Conclusion: Neuro-Fuzzy expert system with the highest level of accuracy and a Decision-Table with the lowest Mean absolute error, which are rule-based models, are the best models. Therefore, these models are recommended as a valuable tool for prediction parameters of ICU as well as medical decision-making.

Application of machine learning methods in predicting schizophrenia and bipolar disorders: A systematic review.

Background and Aim: Schizophrenia and bipolar disorder (BD) are critical and high-risk inherited mental disorders with debilitating symptoms. Worldwide, 3% of the population suffers from these disorders. The mortality rate of these patients is higher compared to other people. Current procedures cannot effectively diagnose these disorders because it takes an average of 10 years from the onset of the first symptoms to the definitive diagnosis of the disease. Machine learning (ML) techniques are used to meet this need. This study aimed to summarize information on the use of ML techniques for predicting schizophrenia and BD to help early and timely diagnosis of the disease. Methods: A systematic literature search included articles published until January 19, 2020 in 3 databases. Two reviewers independently assessed original papers to determine eligibility for inclusion in this review. PRISMA guidelines were followed to conduct the study, and the Prediction Model Risk of Bias Assessment Tool (PROBAST) to assess included papers. Results: In this review, 1243 papers were retrieved through database searches, of which 15 papers were included based on full-text assessment. ML techniques were used to predict schizophrenia and BDs. The main algorithms applied were support vector machine (SVM) (10 studies), random forests (RF) (5 studies), and gradient boosting (GB) (3 studies). Input and output characteristics were very diverse and have been kept to enable future research. RFs algorithms demonstrated significantly higher accuracy and sensitivity than SVM and GB. GB demonstrated significantly higher specificity than SVM and RF. We found no significant difference between RF and SVM in terms of specificity. Conclusion: ML can precisely predict results and assist in making clinical decisions-concerning schizophrenia and BD. RF often performed better than other algorithms in supervised learning tasks. This study identified gaps in the literature and opportunities for future psychological ML research.

Barriers and challenges of using health information technology in home care: A systematic review.

OBJECTIVE: Despite the benefits of applying Information technology (IT) in-home care some challenges may affect the quality of the services. To deal with these challenges, it is required to identify them before providing such services. Therefore, the aim of this study is to systematically determine the challenges and barriers of using health IT in-home care. Moreover, the possible solutions reported in the included studies were examined. MATERIALS AND METHODS: We performed a systematic search of the PubMed, Web of Science, and Embase databases for studies published between January 2010 and January 2020. For quality assessment of the included articles the Critical Appraisal Skills Programme and the Effective Public Health Practice Project checklists were used. The Supporting the Use of Research Evidence (Supporting the Use of Research Evidence (SURE)) framework was used to categorise the identified barriers and challenges. RESULTS: Of 1755 retrieved studies, 47 studies were included. The main barriers and challenges based on the SURE framework were categorised to Facilities (n = 35), Legislation or regulations (n = 19), Knowledge and skills (n = 18), Attitudes regarding programme acceptability, appropriateness and credibility (n = 16), Financial resources (n = 10), Motivation to change (n = 9), and External communication (n = 8). Studies mostly provided solutions regarding challenges related to the usability and functionality of the applied technology. CONCLUSIONS: The results of this study can help policy-makers and managers of health care organisations to be informed regarding the existing barriers, and implement safer and more effective home care systems. Awareness regarding barriers and potential challenges can help to provide optimal IT-based interventions and facilitate providing home services.

Determining the Effect of the Picture Archiving and Communication System (PACS) on Different Dimensions of Users' Work.

The impact of the picture archiving and communication system (PACS) on healthcare costs, information access, image quality, and user workflow has been well studied. However, there is insufficient evidence on the effect of this system on different dimensions of the users' work. The objective of this study was to evaluate the impact of the PACS on different dimensions of users' work (external communication, service quality, user intention to use the PACS, daily routine, and complaints on users) and to compare the opinions of different groups of users about the PACS. This study was performed on the PACS users (n = 72) at Kerman University of Medical Sciences, including radiologists, radiology staff, ward heads, and physicians. Data were collected using a questionnaire consisting of two parts: demographic information of the participants and 5-point Likert scale questions concerning the five dimensions of users' work. Data were analyzed using descriptive statistics, ANOVA, and Pearson's correlation coefficient statistical tests. The mean of scores given by the PACS users was 4.31 ± 0.86 for external communication, 4.18 ± 0.96 for user intention to use the PACS, 3.91 ± 0.7 for service quality, 3.16 ± 0.56 for daily routine, and 3.08 ± 1.05 for complaints on users. Radiologists and radiology staff had a more positive opinion about the PACS than other clinicians such as physicians (P < 0.01, CI = 95%). Factors such as user age (P < 0.01, CI = 95%), job (P < 0.001, CI = 95%), work experience (P < 0.001, CI = 95%), and PACS training method (P=0.037, CI = 95%) were related to the impact of the PACS on different dimensions of users' work. This study showed that the PACS has a positive effect on different dimensions of users' work, especially on external communication, user intention to use the system, and service quality. It is recommended to implement PACSs in medical centers to support users' work and to maintain and strengthen the capabilities and functions of radiology departments.

Machine Learning Models for Image-Based Diagnosis and Prognosis of COVID-19: Systematic Review.

BACKGROUND: Accurate and timely diagnosis and effective prognosis of the disease is important to provide the best possible care for patients with COVID-19 and reduce the burden on the health care system. Machine learning methods can play a vital role in the diagnosis of COVID-19 by processing chest x-ray images. OBJECTIVE: The aim of this study is to summarize information on the use of intelligent models for the diagnosis and prognosis of COVID-19 to help with early and timely diagnosis, minimize prolonged diagnosis, and improve overall health care. METHODS: A systematic search of databases, including PubMed, Web of Science, IEEE, ProQuest, Scopus, bioRxiv, and medRxiv, was performed for COVID-19-related studies published up to May 24, 2020. This study was performed in accordance with the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-analyses) guidelines. All original research articles describing the application of image processing for the prediction and diagnosis of COVID-19 were considered in the analysis. Two reviewers independently assessed the published papers to determine eligibility for inclusion in the analysis. Risk of bias was evaluated using the Prediction Model Risk of Bias Assessment Tool. RESULTS: Of the 629 articles retrieved, 44 articles were included. We identified 4 prognosis models for calculating prediction of disease severity and estimation of confinement time for individual patients, and 40 diagnostic models for detecting COVID-19 from normal or other pneumonias. Most included studies used deep learning methods based on convolutional neural networks, which have been widely used as a classification algorithm. The most frequently reported predictors of prognosis in patients with COVID-19 included age, computed tomography data, gender, comorbidities, symptoms, and laboratory findings. Deep convolutional neural networks obtained better results compared with non-neural network-based methods. Moreover, all of the models were found to be at high risk of bias due to the lack of information about the study population, intended groups, and inappropriate reporting. CONCLUSIONS: Machine learning models used for the diagnosis and prognosis of COVID-19 showed excellent discriminative performance. However, these models were at high risk of bias, because of various reasons such as inadequate information about study participants, randomization process, and the lack of external validation, which may have resulted in the optimistic reporting of these models. Hence, our findings do not recommend any of the current models to be used in practice for the diagnosis and prognosis of COVID-19.

Evaluating hospital information system according to ISO 9241 part 12.

OBJECTIVES: Compliance with standards in designing information systems leads to better utilization and ease of use for users. In this study, the compliance of a widely used hospital information system (HIS) with ISO 9241 part 12 was assessed. METHODS: This applied research is a descriptive, cross-sectional study in which the HIS of 8 hospitals affiliated with Kerman University of Medical Sciences was evaluated based on ISO 9241 part 12. Data were collected by using ISO 9241/12 checklist. The data was analyzed in SPSS 16 using descriptive statistics. RESULTS: The analysis of data showed that the total compliance of the software with the ISO 9241/12 was 72%. The compliance of the software based on different groups of recommendations was 79% with Organization of information, 91% with Graphic objects, and 58% with Coding techniques. Compliance with different subgroups of ISO recommendations ranged from 28% related to "color coding" in coding techniques to 97% related to "General recommendation for graphical objects" in Graphic objects. CONCLUSION: According to this study, the design of a widely used HIS has fairly good compliance with the standard but still suffers from some problems. Considering the role of accurate, valid and timely information in management of the hospitals, and the difficulty of system optimization after implementation, it is necessary that software developers follow existing standards when designing health information systems.

Spatio-temporal mapping of breast and prostate cancers in South Iran from 2014 to 2017.

BACKGROUND: The most common gender-specific malignancies are cancers of the breast and the prostate. In developing countries, cancer screening of all at risk is impractical because of healthcare resource limitations. Thus, determining high-risk areas might be an important first screening step. This study explores incidence patterns of potential high-risk clusters of breast and prostate cancers in southern Iran. METHODS: This cross-sectional study was conducted in the province of Kerman, South Iran. Patient data were aggregated at the county and district levels calculating the incidence rate per 100,000 people both for cancers of the breast and the prostate. We used the natural-break classification with five classes to produce descriptive maps. A spatial clustering analysis (Anselin Local Moran's I) was used to identify potential clusters and outliers in the pattern of these cancers from 2014 to 2017. RESULTS: There were 1350 breast cancer patients (including, 42 male cases) and 478 prostate cancer patients in the province of Kerman, Iran during the study period. After 45 years of age, the number of men with diagnosed prostate cancer increased similarly to that of breast cancer for women after 25 years of age. The age-standardised incidence rate of breast cancer for women showed an increase from 29.93 to 32.27 cases per 100,000 people and that of prostate cancer from 13.93 to 15.47 cases per 100,000 during 2014-2017. Cluster analysis at the county level identified high-high clusters of breast cancer in the north-western part of the province for all years studied, but the analysis at the district level showed high-high clusters for only two of the years. With regard to prostate cancer, cluster analysis at the county and district levels identified high-high clusters in this area of the province for two of the study years. CONCLUSIONS: North-western Kerman had a significantly higher incidence rate of both breast and prostate cancer than the average, which should help in designing tailored screening and surveillance systems. Furthermore, this study generates new hypotheses regarding the potential relationship between increased incidence of cancers in certain geographical areas and environmental risk factors.

Machine learning models in breast cancer survival prediction.

BACKGROUND: Breast cancer is one of the most common cancers with a high mortality rate among women. With the early diagnosis of breast cancer survival will increase from 56% to more than 86%. Therefore, an accurate and reliable system is necessary for the early diagnosis of this cancer. The proposed model is the combination of rules and different machine learning techniques. Machine learning models can help physicians to reduce the number of false decisions. They try to exploit patterns and relationships among a large number of cases and predict the outcome of a disease using historical cases stored in datasets. OBJECTIVE: The objective of this study is to propose a rule-based classification method with machine learning techniques for the prediction of different types of Breast cancer survival. METHODS: We use a dataset with eight attributes that include the records of 900 patients in which 876 patients (97.3%) and 24 (2.7%) patients were females and males respectively. Naive Bayes (NB), Trees Random Forest (TRF), 1-Nearest Neighbor (1NN), AdaBoost (AD), Support Vector Machine (SVM), RBF Network (RBFN), and Multilayer Perceptron (MLP) machine learning techniques with 10-cross fold technique were used with the proposed model for the prediction of breast cancer survival. The performance of machine learning techniques were evaluated with accuracy, precision, sensitivity, specificity, and area under ROC curve. RESULTS: Out of 900 patients, 803 patients and 97 patients were alive and dead, respectively. In this study, Trees Random Forest (TRF) technique showed better results in comparison to other techniques (NB, 1NN, AD, SVM and RBFN, MLP). The accuracy, sensitivity and the area under ROC curve of TRF are 96%, 96%, 93%, respectively. However, 1NN machine learning technique provided poor performance (accuracy 91%, sensitivity 91% and area under ROC curve 78%). CONCLUSIONS: This study demonstrates that Trees Random Forest model (TRF) which is a rule-based classification model was the best model with the highest level of accuracy. Therefore, this model is recommended as a useful tool for breast cancer survival prediction as well as medical decision making.