Mycobacterium Tuberculosis (TB) is an infectious bacterial disease. In 2018, about 10 million people has been diagnosed with tuberculosis (TB) worldwide. Early diagnosis of TB is necessary for effective treatment, higher survival rate, and preventing its further transmission. The gold standard for tuberculosis diagnosis is sputum culture. Nevertheless, posterior-anterior chest radiographs (CXR) is an effective central method with low cost and a relatively low radiation dose for screening TB with immediate results. TB diagnosis from CXR is a challenging task requiring high level of expertise due to the diverse presentation of the disease. Significant intra-class variation and inter-class similarity in CXR images makes TB diagnosis from CXR a

Left atrial volume (LAV) estimation is an important issue for prognosis of some adverse cardiovascular events. Manual estimation of LAV is a tedious and time-consuming labor. LAV measurement is a challenging task due to some factors such as artifacts and speckle noise generated by ultrasound imaging, vague boundaries of anatomical structures, viewpoint variations and different scanning angles. Therefore, using automatic methods for estimating LAV is necessary. In this study, our aim is estimating maximal and minimal LAV from echocardiographic images. Moreover, cardiac cycle phase is identified via recognizing end-systole and end-diastole frames as the main prerequisite of LAV measurement. Different from the previous studies, this study prop

Stillbirth is defined as fetal loss in pregnancy beyond 28 weeks by WHO. In this study, a machine-learning based method is proposed to predict stillbirth from livebirth and discriminate stillbirth before and during delivery and rank the features. A two-step stack ensemble classifier is proposed for classifying the instances into stillbirth and livebirth at the first step and then, classifying stillbirth before delivery from stillbirth during the labor at the second step. The proposed SE has two consecutive layers including the same classifiers. The base classifiers in each layer are decision tree, Gradient boosting classifier, logistics regression, random forest and support vector machines which are trained independently and aggregated ba

In recent years, complex networks have been used as new tools to study patterns in earthquake data. Although various methods have been developed to construct earthquake networks, there is still a long way to use this approach as a complete framework for analyzing seismicity. This research develops novel methods for building earthquake networks and investigates the patterns that they could reveal. The proposed methods use a specific declustered catalog and define nodes based on main shocks and edges on aftershocks’ period or sequence. Another method is offered to convert the resulted networks, as earthquake networks, to epicenters networks. The catalog of Iran’s earthquakes from 2006 to 2018 is used to produce earthquake networks using t

Although arteriovenous fistula is the preferred vascular access method, it has challenges in three phases of planning, maturation, and maintenance. We looked at the root of fistula challenges in the maintenance phase and found traces of inflammation. We investigated the role of systemic inflammation in the maintenance phase to understand its effects on post-maturation function and extract knowledge to help extend fistula longevity. Previous studies on fistula longevity have focused entirely on statistical tests. Since these tests put limitations on data, we also used a data mining framework for data analysis. For predictive analysis, we used the decision tree, random forest, and support vector machines. For inferential analysis, we used the

Background Vitiligo is an acquired pigmentary skin disorder characterized by depigmented macules and patches which brings many challenges for the patients suffering from. For vitiligo severity assessment, several scoring methods have been proposed based on morphometry and colorimetry. But, all methods suffer from much inter‐ and intra‐observer variations for estimating the depigmented area. For all mentioned assessment methods of vitiligo disorder, accurate segmentation of the skin images for lesion detection and localization is required. The image segmentation for localizing vitiligo skin lesions has many challenges because of illumination variation, different shapes and sizes of vitiligo lesions, vague lesion boundaries and skin hai

Laparoscopy or minimally-invasive surgery (MIS) is performed by inserting a camera called endoscope inside the body to display the surgical actions online with the ability to record and archive the video. Recognizing the surgical actions automatically from the laparoscopic videos have many applications such as surgical skill assessment, teaching purposes, and workflow recognition but is a challenging task. The main aim of this study is proposing novel automatic methods for surgical action recognition from the laparoscopic video frames. For this purpose, three different scenarios are designed, evaluated and compared using 5-fold cross validation strategy. The first and the second scenarios are based on deep neural networks and combination of

IntroductionAcute Lymphoblastic Leukemia (ALL) is the most common cancer among children. With the advancements of science and technology, the mortality rate of ALL is highly reduced. The aim of this study is treatment outcome classification of ALL patients aged less than 18 years with clinical and medical data using machine learning. For this purpose, ALL pediatric patients younger than 18 years treated at MAHAK multi-super specialty hospital from 2012 to 2018 are analyzed. Furthermore, MAHAK hospital is a reference center for treatment of childhood malignancies in Iran.DataIn this study, data is collected manually from the paper-based records of 241 patients. Features included are patient demographic characteristics, medical information an

Background: Mycobacterium Tuberculosis (TB) is an infectious bacterial disease presenting similar symptoms to pneumonia; therefore, differentiating between TB and pneumonia is challenging. Therefore, the main aim of this study is proposing an automatic method for differential diagnosis of TB from Pneumonia. Methods: In this study, a two-step decision support system named TPIS is proposed for differential diagnosis of TB from pneumonia based on stacked ensemble classifiers. The first step of our proposed model aims at early diagnosis based on low-cost features including demographic characteristics and patient symptoms (including 18 features). TPIS second step makes the final decision based on the meta features extracted in the first step, th

BackgroundBiometric identification is advantageous over traditional authentication methods such as password, PIN (Personal Identification Number), and/or a token-based card. Electrocardiogram (ECG) signals show unique behavioral characteristics for persons due to their heart morphology and structure which make them more appropriate for human identification. ECGs are safe and more reliable. Related previous models for human identification from ECG signals can be divided into conventional machine learning and deep learning models. In this study, a novel noise-robust stacked ensemble of deep and conventional machine learning models (NRSE-DCML) is proposed for human identification from ECG signals.MethodsNRSE-DCML includes an ensemble of deep c

Air pollution has negative impact on health status of the population. Several previous studies have been assessed the short-term and/or long-term effect of air pollutants on different diseases. An important sign of increasing the number of new people suffering from a disease or worsening the disease among the persons is increasing the hospitalization rate due to the disease. Increasing the incidence rate or severity of mental disorders which leads to patient hospitalization due to these types of diseases have negative impacts on the socio-economic aspects on the affected countries. Therefore, predicting the hospitalization rate due to mental disorders in advance may be helpful for health institutions to be prepared for dealing with these si

Background and Objective: The health industry is a competitive and lucrative industry that has attracted many investors. Therefore, hospitals must create competitive advantages to stay in the competitive market. Patient satisfaction with the services provided in hospitals is one of the most basic competitive advantages of this industry. Therefore, identifying and analyzing the factors affecting the increase of patient satisfaction is an undeniable necessity that has been addressed in this study. Methods: Because patient satisfaction characteristics used in hospitals may have a hidden relationship with each other, data mining approaches and tools to analyze patient satisfaction according to the questionnaire used We used the hospital. After

Background: Acute Lymphoblastic Leukemia (ALL) is the most common blood disease in children and is responsible for the most deaths amongst children. Due to major improvements in the treatment protocols in the 50-years period, the survivability of this disease has witnessed dramatic rise until this date which is about 90 percent. There are many investigations tending to indicate the efficiency of cranial radiotherapy found out that without that, outcome of the patients did not change and even it improved at some cases. Methods: the main aim of this study is predicting cranial radiotherapy treatment in pediatric acute lymphoblastic leukemia patients using machine learning. Scope of this paper is intertwined with predicting the necessity of on