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Abstract Objectives Cancer is a global health challenge with complex characteristics. Despite progress in research and treatment, a universally effective prevention strategy is lacking. Access to reliable information, especially on occurrence rates, is vital for cancer management. This study aims to create a database containing individual and spatially integrated data on commonly diagnosed cancers in Iran from 2014 to 2017, serving as a valuable resource for spatial-epidemiological approaches. Data description This database encompasses several files related to cancer data. The first file is an Excel spreadsheet, containing information on newly diagnosed cancer cases from 2014 to 2017. It provides demographic details and specific characteristics of 482,229 cancer patients. We categorized this data according to the International Agency for Research on Cancer (IARC) reporting rules to identify cancers with the highest incidence. To create a geodatabase, individual data was integrated at the county level and combined with population data. Files 2 and 3 contain gender-specific spatial data for the top cancer types and non-melanoma skin cancer. Each file includes county identifications, the number of cancer cases for each cancer type per year, and gender-specific population information. Lastly, there is a user’s guide file to help navigate through the data files.

Abstract Background Cancer is a significant public health concern and the second leading cause of death. This study aims to visualize spatial patterns of top common cancer types and identify high-risk and low-risk counties for these cancers in Iran from 2014 to 2017. Methods In this study, we analyzed 482,229 newly diagnosed cancer cases recorded by the Iranian National Population-Based Cancer Registry from 2014 to 2017. We employed a purely spatial scanning model and local Moran I analysis to explore spatial patterns across Iran. Results Approximately 53% of all cases were male. The average age of cancer diagnosis was 62.58 ± 17.42 years for males and 56.11 ± 17.33years for females. Stomach cancer was the most common cancer in men. The northern and northwestern regions of Iran were identified as high-risk areas for stomach cancer in both genders, with a relative risk (RR) ranging from 1.26 to 2.64 in males and 1.19 to 3.32 in females. These areas recognized as high-risk areas for trachea, bronchus, and lung (TBL) cancer specifically in males (RR:1.15–2.02). Central regions of Iran were identified as high-risk areas for non-melanoma skin cancers in both genders, ranking as the second most common cancer (RR:1.18–5.93 in males and 1.24–5.38 in females). Furthermore, bladder cancer in males (RR:1.32–2.77) and thyroid cancer in females (RR:1.88–3.10) showed concentration in the central part of Iran. Breast cancer, being the most common cancer among women (RR:1.23–5.54), exhibited concentration in the northern regions of the country. Also, northern regions of Iran were identified as high-risk clusters for colon cancer (RR:1.31–3.31 in males and 1.33–4.13 in females), and prostate cancer in males (RR:1.22–2.31). Brain, nervous system cancer, ranked sixth among women (RR:1.26–5.25) in central areas. Conclusions The study's revelations on the spatial patterns of common cancer incidence in Iran provide crucial insights into the distribution and trends of these diseases. The identification of high-risk areas equips policymakers with valuable information to tailor targeted screening programs, facilitating early diagnosis and effective disease control strategies.

Background: Research has shown that the use the Internet of Things (IoT) and artificial intelligence (AI) can enhance the effectiveness of telemedicine. Therefore, the objective of this study is to systematically investigate the utilization of machine learning (ML)-based IoT during the COVID-19 pandemic. Methods: A comprehensive search of PubMed, Scopus and IEEE was conducted from January 2020 to February 2023 according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA). Results: Out of the 1576 studies identified, 57 were included in the analysis. The use of IoT was 30 % for detection and 22 % for monitoring. Parameters considered included vital signs, diagnostic images, physical symptoms, tests, air quality, and medical history. A review of the sensors revealed that body temperature sensors were of interest in 65 % of the studies. The random forest algorithm was found to be the most common ML model, used in 14 % of the studies. Performance measures were reported in over 90 % of the studies to evaluate the models. Conclusion: This study provides valuable insights into the integration of IoT and ML to address challenges related to COVID-19. This systematic review can serve as a roadmap for advancing research at the intersection of healthcare, IoT, and ML.