Diabetes Diagnosis with Maximum Covariance Weighted Resilience Back Propagation Procedure

Ogunmakinde, Olabode Emmanuel; Olusoga, Fasoranbaku; Abayomi, Fagbuwagun

doi:10.9734/bjmcs/2015/14871

Cited by 3 publications

(1 citation statement)

References 12 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Applications for prediction included developing a risk advisor model to predict the chances of diabetes complication according to changes in risk factors [42], identifying the optimal subset of attributes from a given set of attributes for diagnosis of heart disease [43], modelling daily patient arrivals in the Emergency Department [44]. ANN was applied for diagnosis of disease based on age, sex, body mass index, average blood pressure and blood serum measurements [45], comparing predictive accuracies of different types of ANN and statistical models for diagnosis of coronary artery disease [46], diagnosis and risk group assignment for pulmonary tuberculosis among hospitalized patients [47], and non-invasive diagnosis of early risk in dengue patients [48]. Other examples include exploring the potential use of mobile phones as a health promotional tool by tracking daily exercise activities of people and using ANN to estimate a user’s movement[49], or using ANN to identify factors related to treatment and outcomes potentially impacting patient length of stay[50].…”

Section: Resultsmentioning

confidence: 99%

Applications of artificial neural networks in health care organizational decision-making: A scoping review

2019

View full text Add to dashboard Cite

Health care organizations are leveraging machine-learning techniques, such as artificial neural networks (ANN), to improve delivery of care at a reduced cost. Applications of ANN to diagnosis are well-known; however, ANN are increasingly used to inform health care management decisions. We provide a seminal review of the applications of ANN to health care organizational decision-making. We screened 3,397 articles from six databases with coverage of Health Administration, Computer Science and Business Administration. We extracted study characteristics, aim, methodology and context (including level of analysis) from 80 articles meeting inclusion criteria. Articles were published from 1997–2018 and originated from 24 countries, with a plurality of papers (26 articles) published by authors from the United States. Types of ANN used included ANN (36 articles), feed-forward networks (25 articles), or hybrid models (23 articles); reported accuracy varied from 50% to 100%. The majority of ANN informed decision-making at the micro level (61 articles), between patients and health care providers. Fewer ANN were deployed for intra-organizational (meso- level, 29 articles) and system, policy or inter-organizational (macro- level, 10 articles) decision-making. Our review identifies key characteristics and drivers for market uptake of ANN for health care organizational decision-making to guide further adoption of this technique.

show abstract