Back propagation neural network model for medical expenses in patients with breast cancer

Ruan, Feiyan; Ding, Xiaotong; Li, Huiping; Wang, Yixuan; Ye, Kemin; Kan, Houming

doi:10.3934/mbe.2021185

Cited by 7 publications

(8 citation statements)

References 25 publications

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“…2, is a feedforward neural network with multilayer structure and trained by error back propagation algorithm (Ref. [50]). Recently, BPN still remains one of the popular machine learning methods or an important base for different AI applications, including predicting or classifying many health and medical events (Ref.…”

Section: Back Propagation Neural Networkmentioning

confidence: 99%

“…Recently, BPN still remains one of the popular machine learning methods or an important base for different AI applications, including predicting or classifying many health and medical events (Ref. [50][51][52][53][54][55]), such as BPN associated models for predicting medical expenses and all-cause risk of 30-day readmission, detecting of COVID-19 disease and ischemic stroke, diagnosing hypertrophic cardiomyopathy and hypertensive heart disease, classifying arrythmia disease (with ECG signals), etc.…”

Section: Back Propagation Neural Networkmentioning

confidence: 99%

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Left Ventricular Hypertrophy Detection Using Electrocardiographic Signal

Wu²,

et al. 2022

Preprint

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Left ventricular hypertrophy (LVH) indicates subclinical organ damage, associating with the incidence of cardiovascular diseases. From the medical perspective, electrocardiogram (ECG) is a low-cost, non-invasive, and easily reproducible tool that is often used as a preliminary diagnosis for the detection of heart disease. Nowadays, there are many criteria for assessing LVH by ECG. These criteria usually include that voltage combination of RS peaks in multi-lead ECG must be greater than one or more thresholds for diagnosis. We developed a system for detecting LVH using ECG signals by two steps: firstly, the R-peak and S-valley amplitudes of the 12-lead ECG were extracted to automatically obtain a total of 24 features and ECG beats of each case (LVH or non-LVH) were segmented; secondly, a back propagation neural network (BPN) was trained using a dataset with these features. Echocardiography (ECHO) was used as the gold standard for diagnosing LVH. The number of LVH cases (of a Taiwanese population) identified was 173. As each ECG sequence generally included 8 to 13 cycles (heartbeats) due to differences in heart rate, etc., we identified 1466 ECG cycles of LVH patients after beat segmentation. Results showed that our BPN model for detecting LVH reached the testing accuracy, precision, sensitivity, and specificity of 0.961, 0.958, 0.966 and 0.956, respectively. Detection performances of our BPN model, on the whole, outperform 7 methods using ECG criteria and many ECG-based artificial intelligence (AI) models reported previously for detecting LVH.

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Section: Back Propagation Neural Networkmentioning

confidence: 99%

Section: Back Propagation Neural Networkmentioning

confidence: 99%

Left Ventricular Hypertrophy Detection Using Electrocardiographic Signal

Wu²,

et al. 2022

Preprint

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“…In general, the standard three-layer BP neural network can achieve relatively accurate prediction [24]. erefore, three-layer network structure is adopted to construct the prediction model.…”

Section: Determination Of Network Structurementioning

confidence: 99%

Construction of Surface Water Pollution Prediction Model Based on Machine Learning

Che

Wei

Wen³

2022

Scientific Programming

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Existing prediction models have low prediction accuracy for surface water pollution with many influencing factors. Taking algal bloom prediction as the entry point of surface water pollution research, LASSO-LARS algorithm is adopted to select the main factors affecting algal bloom variables. At the same time, combined with BP neural network in machine learning, a surface water pollution prediction model based on machine learning is proposed. The results show that the proposed method can accurately predict the algal blooms by using the BP neural network algal bloom prediction model, where the LASSO-LARS algorithm is used to select the variables, such as water temperature, pH, transparency, conductivity, dissolved oxygen, ammonia nitrogen, and chlorophyll a, as model inputs, and the relative error of prediction is less than 5.2%. Thus, the proposed method has certain validity and practical application value.

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“…With the development of artificial intelligence technology, the artificial intelligence (AI) algorithm represented by deep learning is gradually applied in various medical fields and has achieved better diagnostic energy efficiency [ 13 – 16 ]. Among them, the back propagation (BP) neural network model is widely used to establish a prediction model in medical diagnosis [ 17 ].…”

Section: Introductionmentioning

confidence: 99%

Prediction of Lung Infection during Palliative Chemotherapy of Lung Cancer Based on Artificial Neural Network

Guo

Gao

Dai

et al. 2022

Computational and Mathematical Methods in Medicine

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Lung infection seriously affects the effect of chemotherapy in patients with lung cancer and increases pain. The study is aimed at establishing the prediction model of infection in patients with lung cancer during chemotherapy by an artificial neural network (ANN). Based on the data of historical cases in our hospital, the variables were screened, and the prediction model was established. A logistic regression (LR) model was used to screen the data. The indexes with statistical significance were selected, and the LR model and back propagation neural network model were established. A total of 80 cases of advanced lung cancer patients with palliative chemotherapy were predicted, and the prediction performance of different model was evaluated by the receiver operating characteristic curve (ROC). It was found that age ≧ 60 years, length of stay ≧ 14 d, surgery history, combined chemotherapy, myelosuppression, diabetes, and hormone application were risk factors of infection in lung cancer patients during chemotherapy. The area under the ROC curve of the LR model for prediction lung infection was 0.729 ± 0.084 , which was less than that of the ANN model ( 0.897 ± 0.045 ). The results concluded that the neural network model is better than the LR model in predicting lung infection of lung cancer patients during chemotherapy.

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Back propagation neural network model for medical expenses in patients with breast cancer

Cited by 7 publications

References 25 publications

Left Ventricular Hypertrophy Detection Using Electrocardiographic Signal

Left Ventricular Hypertrophy Detection Using Electrocardiographic Signal

Construction of Surface Water Pollution Prediction Model Based on Machine Learning

Prediction of Lung Infection during Palliative Chemotherapy of Lung Cancer Based on Artificial Neural Network

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