Multi-Class Neural Networks to Predict Lung Cancer

Rajan, Juliet Rani; Chilambuchelvan, A.; Duela, J. Shiny

doi:10.1007/s10916-019-1355-9

Cited by 10 publications

(9 citation statements)

References 11 publications

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“…They are widely used for classification purposes and can be intuitive [3]. ANNs are developed on the basis of biological neurons of the human brain and trained to generate an output outcome as a weighted combination of AGING the input variables [29,30]. They aim to solve a variety of classification or pattern recognition problems [26].…”

Section: Discussionmentioning

confidence: 99%

Development of a machine learning-based multimode diagnosis system for lung cancer

Duan

Cao

Hong

et al. 2020

Aging

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As an emerging technology, artificial intelligence has been applied to identify various physical disorders. Here, we developed a three-layer diagnosis system for lung cancer, in which three machine learning approaches including decision tree C5.0, artificial neural network (ANN) and support vector machine (SVM) were involved. The area under the curve (AUC) was employed to evaluate their decision powers. In the first layer, the AUCs of C5.0, ANN and SVM were 0.676, 0.736 and 0.640, ANN was better than C5.0 and SVM. In the second layer, ANN was similar with SVM but superior to C5.0 supported by the AUCs of 0.804, 0.889 and 0.825. Much higher AUCs of 0.908, 0.910 and 0.849 were identified in the third layer, where the highest sensitivity of 94.12% was found in C5.0. These data proposed a three-layer diagnosis system for lung cancer: ANN was used as a broadspectrum screening subsystem basing on 14 epidemiological data and clinical symptoms, which was firstly adopted to screen high-risk groups; then, combining with additional 5 tumor biomarkers, ANN was used as an auxiliary diagnosis subsystem to determine the suspected lung cancer patients; C5.0 was finally employed to confirm lung cancer patients basing on 22 CT nodule-based radiomic features.

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Section: Discussionmentioning

confidence: 99%

Development of a machine learning-based multimode diagnosis system for lung cancer

Duan

Cao

Hong

et al. 2020

Aging

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“…The basic structure of the multi-class neural network has an input layer, hidden layer, and output layer [24]. When the neural network is applied to multi-classification tasks, the softmax function should be used as the activation function in the final output layer, so that the model can calculate the classification probabilities of multiple categories simultaneously, with the category with the highest probability being the final diagnosis output [25].…”

Section: Multiple Infectious Disease Diagnostic Model (Middm)mentioning

confidence: 99%

Deep learning model for multi-classification of infectious diseases from unstructured electronic medical records

Wang

Wei²,

Jia

et al. 2022

BMC Med Inform Decis Mak

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Purpose Predictively diagnosing infectious diseases helps in providing better treatment and enhances the prevention and control of such diseases. This study uses actual data from a hospital. A multiple infectious disease diagnostic model (MIDDM) is designed for conducting multi-classification of infectious diseases so as to assist in clinical infectious-disease decision-making. Methods Based on actual hospital medical records of infectious diseases from December 2012 to December 2020, a deep learning model for multi-classification research on infectious diseases is constructed. The data includes 20,620 cases covering seven types of infectious diseases, including outpatients and inpatients, of which training data accounted for 80%, i.e., 16,496 cases, and test data accounted for 20%, i.e., 4124 cases. Through the auto-encoder, data normalization and sparse data densification processing are carried out to improve the model training effect. A residual network and attention mechanism are introduced into the MIDDM model to improve the performance of the model. Result MIDDM achieved improved prediction results in diagnosing seven kinds of infectious diseases. In the case of similar disease diagnosis characteristics and similar interference factors, the prediction accuracy of disease classification with more sample data is significantly higher than the prediction accuracy of disease classification with fewer sample data. For instance, the training data for viral hepatitis, influenza, and hand foot and mouth disease were 2954, 3924, and 3015 respectively and the corresponding test accuracy rates were 99.86%, 98.47%, and 97.31%. There is less training data for syphilis, infectious diarrhea, and measles, i.e., 1208, 575, and 190 respectively and the corresponding test accuracy rates were noticeably lower, i.e., 83.03%, 87.30%, and42.11%. We also compared the MIDDM model with the models used in other studies. Using the same input data, taking viral hepatitis as an example, the accuracy of MIDDM is 99.44%, which is significantly higher than that of XGBoost (96.19%), Decision tree (90.13%), Bayesian method (85.19%), and logistic regression (91.26%). Other diseases were also significantly better predicted by MIDDM than by these three models. Conclusion The application of the MIDDM model to multi-class diagnosis and prediction of infectious diseases can improve the accuracy of infectious-disease diagnosis. However, these results need to be further confirmed via clinical randomized controlled trials.

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“…It performs well in dealing with the problem of non-normal distribution and is responsible for many of the recent advances in artificial intelligence [28,29]. For example, the NN structure is widely used in identity recognition, image analysis, environmental detection, and medical diagnoses [30][31][32][33]. e criterion of optimization in the neural network is to make the error of the training set or the test set the smallest [34].…”

Section: Introductionmentioning

confidence: 99%

A Dual-Network Modeling Architecture for Statistical Evaluation of College Graduates’ Working Ability in Consistence with Their Job Position and Remuneration

Hong

Yang

Wen

et al. 2022

Computational Intelligence and Neuroscience

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Optimal human resources allocation asks to employ a person to work in the position corresponding to his/her ability. Employment competence is the key feedback to the cultivation of college students’ working ability. The data relationship needs to analyze between the in-school cultivation items and the working abilities required by the companies. Machine learning framework is introduced to study the companies’ responses to the cultivation of college students. In this work, a dual-network architecture is built up for statistical modeling evaluation of college graduates’ working ability in consistence with their job position and remuneration. A requirement network and a cultivation network are constructed for extracting features from the original working ability data required by companies and cultivated ever in school. The networks are fully trained by adaptively tuning the linking weights. The extracted features are fused together to estimate the working competence of each target sample/person. To evaluate the dual-network model, a modeling index system is designed, including proposing a total evaluation index calculus for the dual-network model, and a variable importance index from the original data. The samples are consequently ranked by the model predicted index and by the variable importance index, respectively. The ranking difference is used to evaluate the prediction efficiency of the dual-network model. Experimental results show that the dual network architecture is feasible to establish statistical models for the evaluation of college graduates’ in-school cultivated working ability in consistence with the company’s required working ability at their job position and their deserved remuneration.

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Multi-Class Neural Networks to Predict Lung Cancer

Cited by 10 publications

References 11 publications

Development of a machine learning-based multimode diagnosis system for lung cancer

Development of a machine learning-based multimode diagnosis system for lung cancer

Deep learning model for multi-classification of infectious diseases from unstructured electronic medical records

A Dual-Network Modeling Architecture for Statistical Evaluation of College Graduates’ Working Ability in Consistence with Their Job Position and Remuneration

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