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Healthcare Analytics is a peer-reviewed journal published by Elsevier, covering Artificial Intelligence in Healthcare, COVID-19 epidemiological studies, Machine Learning in Healthcare.
| Publisher | Elsevier |
|---|---|
| Reference style | Numbered (Elsevier) Numbered — [1], [2] in the text [1] A. Smith, B. Jones, C. Lee, A representative article title, Healthcare Analytics 12 (2023) 45–58.
Formats any DOI in Healthcare Analytics style. No sign-up. |
| Publishes research in | Artificial Intelligence in Healthcare COVID-19 epidemiological studies Machine Learning in Healthcare AI in cancer detection Mathematical and Theoretical Epidemiology and Ecology Models |
| ISSN | 2772-4425 |
| Citation impact (2-yr) | 9.53 |
| h-index | 51 |
| i10-index | 212 |
| Total citations | 9,349 |
| Article processing charge | $1,500 |
| Open access | Yes |
| Top institutions publishing here | Daffodil International University |
| Journal website | www.journals.elsevier.com |
| You get | A submission-ready PDF and the editable LaTeX source — ready to submit. |
Citations each year’s papers have accumulated so far — the most recent years are still building up.
The paper focuses on the construction of an artificial intelligence-based heart disease detection system using machine learning algorithms. We show how machine learning can help predict whether a person will develop heart disease. In this paper, a python-based application is developed for healthcare research as it is more reliable and helps track and establish different…
Artificial Intelligence (AI) solutions have been widely used in healthcare, and recent developments in deep neural networks have contributed to significant advances in medical image processing. Much ongoing research is aimed at helping medical practitioners by providing automated systems to analyze images and diagnose acute diseases, such as brain tumors, bone cancer, breast cancer, bone…
The negative impact of stroke in society has led to concerted efforts to improve the management and diagnosis of stroke. With an increased synergy between technology and medical diagnosis, caregivers create opportunities for better patient management by systematically mining and archiving the patients’ medical records. Therefore, it is vital to study the interdependency of these…
Skin diseases are common health problems around the world. The perils of the infections are invisible, which cause physical health distress as well as initiate mental depression. In addition, it sometimes leads to skin cancer in severe cases. Subsequently, diagnosing skin diseases from clinical images is one of the foremost challenging tasks in medical image…
Heart disease remains the leading cause of death, such that nearly one-third of all deaths worldwide are estimated to be caused by heart-related conditions. Advancing applications of classification-based machine learning to medicine facilitates earlier detection. In this study, the Classification and Regression Tree (CART) algorithm, a supervised machine learning method, has been employed to predict…
