Journal of Education and Health Promotion

Predicting intubation risk among COVID‑19 hospitalized patients using artificial neural networks

. Raoof Nopour; . Mostafa Shanbezadeh; . Hadi Kazemi-Arpanahi

Volume 13, Issue 1 , January 2023, , Pages 1-8

Abstract

BACKGROUND: Accurately predicting the intubation risk in COVID‑19 patients at the admissiontime is critical to optimal use of limited hospital resources, providing customized and ... Read More

Developing the breast cancer risk prediction system using hybrid machine learning algorithms

. Mohammad R. Afrash; . Azadeh Bayani; . Mostafa Shanbehzadeh; . Mohammadkarim Bahadori; . Hadi Kazemi‑Arpanahi

Volume 12, Issue 7 , August 2022, , Pages 1-12

Abstract

BACKGROUND: Breast cancer (BC) is the most common cause of cancer‑related deaths in womenglobally. Currently, many machine learning (ML)‑based predictive models have been established ... Read More BACKGROUND: Breast cancer (BC) is the most common cause of cancer‑related deaths in womenglobally. Currently, many machine learning (ML)‑based predictive models have been established toassist clinicians in decision making for the prediction of BC. However, preventing risk factor formationeven with having healthy lifestyle behaviors or preventing disease at early stages can significantlylead to optimal population‑wide BC health. Thus, we aimed to develop a prediction model by using agenetic algorithm (GA) incorporating several ML algorithms for the prediction and early warning of BC.MATERIAL AND METHODS: The data of 3168 healthy individuals and 1742 patient case recordsin the BC Registry Database in Ayatollah Taleghani hospital, Abadan, Iran were analyzed. First, amodified hybrid GA was used to perform feature selection and optimization of selected features.Then, with the use of selected features, several ML algorithms were trained to predict BC. Afterward,the performance of each model was measured in terms of accuracy, precision, sensitivity, specificity,and receiver operating characteristic (ROC) curve metrics. Finally, a clinical decision support systembased on the best model was developed.RESULTS: After performing feature selection, age, consumption of dairy products, BC family history,breast biopsy, chest X‑ray, hormone therapy, alcohol consumption, being overweight, having children,and education statuses were selected as the most important features for prediction of BC. Theexperimental results showed that the decision tree yielded a superior performance than other MLmodels, with values of 99.3%, 99.5%, 98.26% for accuracy, specificity, and sensitivity, respectively.CONCLUSION: The developed predictive system can accurately identify persons who are at elevatedrisk for BC and can be used as an essential clinical screening tool for the early prevention of BC andserve as an important tool for developing preventive health strategies.

Artificial intelligence: A new tool in surgeon’s hand

. Amit Gupta; . Tanuj Singla; . Jaine John Chennatt; . Lena Elizabath David; . Shaik Sameer Ahmed; . Deepak Rajput

Volume 12, Issue 3 , March 2022, , Pages 1-6

Abstract

Artificial intelligence (AI) is the future of surgery. Technological advancements are taking place at an incredible pace, largely due to AI or AI‑backed systems. It is likely ... Read More

Developing an artificial neural network for detecting COVID‑19 disease

. Mostafa Shanbehzadeh; . Raoof Nopour; . Hadi Kazemi-Arpanahi

Volume 12, Issue 1 , January 2022, , Pages 1-10

Abstract

BACKGROUND: From December 2019, atypical pneumonia termed COVID‑19 has been increasingexponentially across the world. It poses a great threat and challenge to world health and the ... Read More

Performance analysis of data mining algorithms for diagnosing COVID‑19

. Raoof Nopour; . Hadi Kazemi‑Arpanahi; . Mostafa Shanbehzadeh; . Akbar Azizifar

Volume 11, Issue 10 , November 2021, , Pages 1-8

Abstract

BACKGROUND: An outbreak of atypical pneumonia termed COVID‑19 has widely spread all overthe world since the beginning of 2020. In this regard, designing a prediction system for the ... Read More

Performance evaluation of selected machine learning algorithms for COVID‑19 prediction using routine clinical data: With versus Without CT scan features

. Mostafa Shanbehzadeh; . Hadi Kazemi-Arpanahi; . Azam Orooji; . Sara Mobarak; . Saeed Jelvay

Volume 11, Issue 7 , August 2021, , Pages 1-11

Abstract

BACKGROUND: Given coronavirus disease (COVID‑19’s) unknown nature, diagnosis, and treatmentis very complex up to the present time. Thus, it is essential to have a framework ... Read More

Journal of Education and Health Promotion

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Volume 13 (2023)

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Volume 11 (2021)

Volume 10 (2020)

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Volume 3 (2013)

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Volume 1 (1900)

Keywords = machine learning

Predicting intubation risk among COVID‑19 hospitalized patients using artificial neural networks

Abstract

Developing the breast cancer risk prediction system using hybrid machine learning algorithms

Abstract

Artificial intelligence: A new tool in surgeon’s hand

Abstract

Developing an artificial neural network for detecting COVID‑19 disease

Abstract

Performance analysis of data mining algorithms for diagnosing COVID‑19

Abstract

Performance evaluation of selected machine learning algorithms for COVID‑19 prediction using routine clinical data: With versus Without CT scan features

Abstract