Assessment of HIV/AIDS-related health performance using an artificial neural network

Lee, Chang Won; Park, Jung-A

doi:10.1016/s0378-7206(00)00068-9

Cited by 27 publications

(11 citation statements)

References 22 publications

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“…The ANN model supposes that nodes (ie neurons) have their own values for processing data, and values are passed through connections through neurons. Then, each link has a weight that replicates the values transmitted in a neural network and applies an activation function to its input to determine the output value of each neuron (Cho, Sthiannopkao, Pachepsky, Kim, & Kim, 2011;Iliadis & Maris, 2007;Lee & Park, 2001;Tagluk & D Işık, 2019). Last few years, it is commonly used in health and social science such as estimation of patient health management by using data of warnings, alerts, events and precautions (Ghavami & Kapur, 2012).…”

Section: Simulation: Artificial Neural Network Modelmentioning

confidence: 99%

Role of the Health System in Combating Covid-19: Cross-Section Analysis and Artificial Neural Network Simulation for 124 Country Cases

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et al. 2020

Social Work in Public Health

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Section: Simulation: Artificial Neural Network Modelmentioning

confidence: 99%

Role of the Health System in Combating Covid-19: Cross-Section Analysis and Artificial Neural Network Simulation for 124 Country Cases

Bayraktar

Özyılmaz

Toprak

et al. 2020

Social Work in Public Health

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“…The study used CRISP-DM methodology to develop the predictive model. The result of the study had indicated that 81.8% performance was achieved to associate the attribute with HIV status [31]. The researcher has also recommended that in order to scale up HIV testing the implementation of the same research problem with other data mining techniques by increasing the number of dataset, number of variables could provide better performance.…”

Section: Application Of Data Mining In Hiv Testingmentioning

confidence: 99%

Comparing Data Mining Techniques in HIV Testing Prediction

Hailu¹

2015

IIM

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Introduction: The present work compared the prediction power of the different data mining techniques used to develop the HIV testing prediction model. Four popular data mining algorithms (Decision tree, Naive Bayes, Neural network, logistic regression) were used to build the model that predicts whether an individual was being tested for HIV among adults in Ethiopia using EDHS 2011. The final experimentation results indicated that the decision tree (random tree algorithm) performed the best with accuracy of 96%, the decision tree induction method (J48) came out to be the second best with a classification accuracy of 79%, followed by neural network (78%). Logistic regression has also achieved the least classification accuracy of 74%. Objectives: The objective of this study is to compare the prediction power of the different data mining techniques used to develop the HIV testing prediction model. Methods: Cross-Industry Standard Process for Data Mining (CRISP-DM) was used to predict the model for HIV testing and explore association rules between HIV testing and the selected attributes. Data preprocessing was performed and missing values for the categorical variable were replaced by the modal value of the variable. Different data mining techniques were used to build the predictive model. Results: The target dataset contained 30,625 study participants. Out of which 16,515 (54%) participants were women while the rest 14,110 (46%) were men. The age of the participants in the dataset ranged from 15 to 59 years old with modal age of 15-19 years old. Among the study participants, 17,719 (58%) have never been tested for HIV while the rest 12,906 (42%) had been tested. Residence, educational level, wealth index, HIV related stigma, knowledge related to HIV, region, age group, risky sexual behaviour attributes, knowledge about where to test for HIV and knowledge on family planning through mass media were found to be predictors for HIV testing. Conclusion and Recommendation: The results obtained from this research reveal that data mining is crucial in extracting relevant information for the effective utilization of HIV testing services which has clinical, community and public health importance at all levels. It is vital to apply different data mining techniques for the same settings and compare the model performances (based on accuracy, sensitivity, and specificity) with each other. Furthermore, this study would also invite interested researchers to explore more T. G. Hailu 154 on the application of data mining techniques in healthcare industry or else in related and similar settings for the future.

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“…A recent survey of AI applications in health care reported uses in major disease areas such as cancer or cardiology and artificial neural networks (ANN) as a common machine learning technique [10]. Applications of ANN in health care include clinical diagnosis, prediction of cancer, speech recognition, prediction of length of stay [11], image analysis and interpretation [12] (e.g. automated electrocardiographic (ECG) interpretation used to diagnose myocardial infarction [13]), and drug development[12].…”

Section: Introductionmentioning

confidence: 99%

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

2019

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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.

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Assessment of HIV/AIDS-related health performance using an artificial neural network

Cited by 27 publications

References 22 publications

Role of the Health System in Combating Covid-19: Cross-Section Analysis and Artificial Neural Network Simulation for 124 Country Cases

Role of the Health System in Combating Covid-19: Cross-Section Analysis and Artificial Neural Network Simulation for 124 Country Cases

Comparing Data Mining Techniques in HIV Testing Prediction

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

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