Predicting Active NBA Players Most Likely to Be Inducted into the Basketball Hall of Famers Using Artificial Neural Networks in Microsoft Excel: Development and Usability Study

Chou, Po‐Hsin; Chien, Tsair‐Wei; Yang, Ting-Ya; Yeh, Yu-Tsen; Chou, Willy; Yeh, Chao‐Hung

doi:10.3390/ijerph18084256

Cited by 8 publications

(15 citation statements)

References 26 publications

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“…The multi-classification module can be done by adding the layers on CNN. Any other types of self-assessment, such as predicting the 14-day hospital readmission of patients with pneumonia, [ 13 ] predicting active NBA players most likely to be inducted into the basketball hall of Famers, [ 14 ] and screening BC, [ 7 ] can apply the CNN model to predict and classify the levels of harmfulness and disease in the future.…”

Section: Discussionmentioning

confidence: 99%

An app to classify a 5-year survival in patients with breast cancer using the convolutional neural networks (CNN) in Microsoft Excel

et al. 2022

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Background: Breast cancer (BC) is the most common malignant cancer in women. A predictive model is required to predict the 5-year survival in patients with BC (5YSPBC) and improve the treatment quality by increasing their survival rate. However, no reports in literature about apps developed and designed in medical practice to classify the 5YSPBC. This study aimed to build a model to develop an app for an automatically accurate classification of the 5YSPBC. Methods: A total of 1810 patients with BC were recruited in a hospital in Taiwan from the secondary data with codes on 53 characteristic variables that were endorsed by professional staff clerks as of December 31, 2019. Five models (i.e., revolution neural network [CNN], artificial neural network, Naïve Bayes, K-nearest Neighbors Algorithm, and Logistic regression) and 3 tasks (i.e., extraction of feature variables, model comparison in accuracy [ACC] and stability, and app development) were performed to achieve the goal of developing an app to predict the 5YSPBC. The sensitivity, specificity, and receiver operating characteristic curve (area under ROC curve) on models across 2 scenarios of training (70%) and testing (30%) sets were compared. An app predicting the 5YSPBC was developed involving the model estimated parameters for a website assessment. Results: We observed that the 15-variable CNN model yields higher ACC rates (0.87 and 0.86) with area under ROC curves of 0.80 and 0.78 (95% confidence interval 0.78–82 and 0.74–81) based on 1357 training and 540 testing cases an available app for patients predicting the 5YSPBC was successfully developed and demonstrated in this study. Conclusion: The 15-variable CNN model with 38 parameters estimated using CNN for improving the ACC of the 5YSPBC has been particularly demonstrated in Microsoft Excel. An app developed for helping clinicians assess the 5YSPBC in clinical settings is required for application in the future.

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Section: Discussionmentioning

confidence: 99%

An app to classify a 5-year survival in patients with breast cancer using the convolutional neural networks (CNN) in Microsoft Excel

et al. 2022

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“…The different types of algorithms for classification in machine learning [ 60 , 61 ] are logistic regression, support vector machine [ 61 ], naïve Bayes, random forest classification, ANN, CNN [ 38 , 39 , 40 , 41 ], and k-nearest neighbor [ 61 ]. ANN was superior to the other algorithms, with a 93.2% classification accuracy in a previous study [ 60 ].…”

Section: Discussionmentioning

confidence: 99%

“…None of the articles used MS Excel to perform the ANN. The interpretations of the ANN concept and process as well as the parameter estimations, are shown in Figure 1 , Appendix B , and the app [ 41 , 42 , 43 , 44 , 45 ]. Readers can estimate the parameters in the ANN model on their own and can examine the differences between their results and that from the current study.…”

Section: Discussionmentioning

confidence: 99%

“…The artificial neural network (ANN) and convolutional neural network (CNN) were analyzed using the four scenarios previously mentioned. The CNN has traditionally been performed on Microsoft (MS) Excel (Microsoft Corp., New York, NY, USA) [ 38 , 39 , 40 , 41 ]. As illustrated in Figure 1 , the ANN process involves data input in Layer 1, where the data are combined with two types of parameters and run through the sigmoid function algorithms in Layers 2 and 3.…”

Section: Methodsmentioning

confidence: 99%

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Predicting the 14-Day Hospital Readmission of Patients with Pneumonia Using Artificial Neural Networks (ANN)

Tey

Chien

Hsu

et al. 2021

IJERPH

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Unplanned patient readmission (UPRA) is frequent and costly in healthcare settings. No indicators during hospitalization have been suggested to clinicians as useful for identifying patients at high risk of UPRA. This study aimed to create a prediction model for the early detection of 14-day UPRA of patients with pneumonia. We downloaded the data of patients with pneumonia as the primary disease (e.g., ICD-10:J12*-J18*) at three hospitals in Taiwan from 2016 to 2018. A total of 21,892 cases (1208 (6%) for UPRA) were collected. Two models, namely, artificial neural network (ANN) and convolutional neural network (CNN), were compared using the training (n = 15,324; ≅70%) and test (n = 6568; ≅30%) sets to verify the model accuracy. An app was developed for the prediction and classification of UPRA. We observed that (i) the 17 feature variables extracted in this study yielded a high area under the receiver operating characteristic curve of 0.75 using the ANN model and that (ii) the ANN exhibited better AUC (0.73) than the CNN (0.50), and (iii) a ready and available app for predicting UHA was developed. The app could help clinicians predict UPRA of patients with pneumonia at an early stage and enable them to formulate preparedness plans near or after patient discharge from hospitalization.

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“…We calculated the sensitivity, specificity, AUC, and CIs along with the accuracy and precision across the proposed models in comparison using equations (1) to (12). Both AUCs in the training and testing sets were compared to assess the model accuracy and stability [34,35].…”

Section: Comparing the Accuracies In Models (Task 2)mentioning

confidence: 99%

Web-Based Skin Cancer Assessment and Classification Using Machine Learning and Mobile Computerized Adaptive Testing in a Rasch Model: Development Study

Yang¹,

Chien²,

Lai³

2022

JMIR Med Inform

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Background Web-based computerized adaptive testing (CAT) implementation of the skin cancer (SC) risk scale could substantially reduce participant burden without compromising measurement precision. However, the CAT of SC classification has not been reported in academics thus far. Objective We aim to build a CAT-based model using machine learning to develop an app for automatic classification of SC to help patients assess the risk at an early stage. Methods We extracted data from a population-based Australian cohort study of SC risk (N=43,794) using the Rasch simulation scheme. All 30 feature items were calibrated using the Rasch partial credit model. A total of 1000 cases following a normal distribution (mean 0, SD 1) based on the item and threshold difficulties were simulated using three techniques of machine learning—naïve Bayes, k-nearest neighbors, and logistic regression—to compare the model accuracy in training and testing data sets with a proportion of 70:30, where the former was used to predict the latter. We calculated the sensitivity, specificity, receiver operating characteristic curve (area under the curve [AUC]), and CIs along with the accuracy and precision across the proposed models for comparison. An app that classifies the SC risk of the respondent was developed. Results We observed that the 30-item k-nearest neighbors model yielded higher AUC values of 99% and 91% for the 700 training and 300 testing cases, respectively, than its 2 counterparts using the hold-out validation but had lower AUC values of 85% (95% CI 83%-87%) in the k-fold cross-validation and that an app that predicts SC classification for patients was successfully developed and demonstrated in this study. Conclusions The 30-item SC prediction model, combined with the Rasch web-based CAT, is recommended for classifying SC in patients. An app we developed to help patients self-assess SC risk at an early stage is required for application in the future.

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Predicting Active NBA Players Most Likely to Be Inducted into the Basketball Hall of Famers Using Artificial Neural Networks in Microsoft Excel: Development and Usability Study

Cited by 8 publications

References 26 publications

An app to classify a 5-year survival in patients with breast cancer using the convolutional neural networks (CNN) in Microsoft Excel

An app to classify a 5-year survival in patients with breast cancer using the convolutional neural networks (CNN) in Microsoft Excel

Predicting the 14-Day Hospital Readmission of Patients with Pneumonia Using Artificial Neural Networks (ANN)

Web-Based Skin Cancer Assessment and Classification Using Machine Learning and Mobile Computerized Adaptive Testing in a Rasch Model: Development Study

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