This study aimed to develop a model predict fasting blood glucose status using machine learning and data mining, since the early diagnosis treatment of diabetes can improve outcomes quality life. crosssectional analyzed from 3376 adults over 30 years old at 16 comprehensive health service centers in Tehran, Iran who participated screening program. The dataset was balanced random sampling synthetic minority over-sampling technique (SMOTE). split into training set (80%) test (20%). Shapley...

Abstract Large language models (LLMs), like ChatGPT, Google’s Bard, and Anthropic’s Claude, showcase remarkable natural processing capabilities. Evaluating their proficiency in specialized domains such as neurophysiology is crucial understanding utility research, education, clinical applications. This study aims to assess compare the effectiveness of Language Models (LLMs) answering questions both English Persian (Farsi) covering a range topics cognitive levels. Twenty four (general, sensory...

The use of traditional medicine has increased in many countries over the past decades. However, computer applications have been rarely developed and used this field. This study aimed to develop a mobile-based disease ontology for Persian medicine. research was carried out two phases. In first phase, Methontology method development, evaluated terms accuracy domain coverage. second application developed, its usability by specialists. Initially, diseases were divided into 24 groups. results...

Introduction: Digital health technologies are transforming healthcare delivery globally. The purpose of the current study was to identify and map status digital applications in Iran through providing graphical/tabular classifications on studies conducted this field.Material Methods: Following PRISMA guidelines, relevant English-language papers published from 2012 until 2023 online scientific databases, including PubMed, Scopus, Web Science IEEE Xplore were screened. A total 97 selected for...

In Persian medicine, early diagnosis and treatment of stomach dystemperament is crucial for preventing other diseases. However, traditional medicine often involves ambiguous less structured information making it challenging practitioners. Integrating fuzzy ontology with case-based reasoning (CBR) systems can enhance diagnostic accuracy in this filed.

<title>Abstract</title> <bold>Background: </bold>Large language models (LLMs), such as ChatGPT, Google's Bard, and Anthropic's Claude, demonstrate impressive natural capabilities. Assessing their competence in specialized domains neurophysiology is important for determining utility research, education, clinical applications. <bold>Objectives:</bold>This study evaluates compares the performance of LLMs answering questions English Persian across different topics cognitive levels....

Machine translation is a process of automatic from one language to another. In the present study, architecture was proposed for system example-based machine English Farsi with help WordNet ontology. This uses knowledge base containing set examples, bilingual dictionary and WordNet. An effective factor in quality ambiguity selecting different meanings that word can have while being translated method WorldNet utilized formation phase example adaptation so be reduced selection correct meaning...

<sec> <title>BACKGROUND</title> Introduction: Currently, diabetes is known as one of the major health problems and most important issue in medical profession which has a high prevalence children adults. On other hand, machine learning been introduced developing, reliable, supportive technology field health, interesting techniques for analyzing interventions, diseases, conditions system use data mining. In fact, mining process selecting, exploring, modeling large amounts data. </sec>...

The Effects of WW2/WW3 Domains Smurf2 Molecule on CD4+CD25+/CD4+ Proportion in Spleen 4T1 Tumor Bearing BALB/c Mice