Characteristics estimation of natural fibre reinforced plastic composites using deep multi-layer perceptron (MLP) technique

Sathish, T.; Sunagar, Prashant; Singh, Vikash; Boopathi, Sampath; sathyamurthy, Ravishankar; Al-Enizi, Abdullah M.; Pandit, Bidhan; Gupta, Manish; Sehgal, Satbir S.

doi:10.1016/j.chemosphere.2023.139346

Cited by 36 publications

(1 citation statement)

References 14 publications

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“…Multilayer Perceptron (MLP) addresses linearly inseparable problems by stacking multiple layers of linear classifiers with nonlinear activation functions. It includes input, hidden, and output layers characterized by weights, biases, and activation functions, commonly employing the Sigmoid function for nonlinear mappings within the specified output range 21 …”

Section: Methodsmentioning

confidence: 99%

Application of interpretable machine learning algorithms to predict distant metastasis in ovarian clear cell carcinoma

Guo,

Xie,

Zhong

et al. 2024

Cancer Medicine

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BackgroundOvarian clear cell carcinoma (OCCC) represents a subtype of ovarian epithelial carcinoma (OEC) known for its limited responsiveness to chemotherapy, and the onset of distant metastasis significantly impacts patient prognoses. This study aimed to identify potential risk factors contributing to the occurrence of distant metastasis in OCCC.MethodsUtilizing the Surveillance, Epidemiology, and End Results (SEER) database, we identified patients diagnosed with OCCC between 2004 and 2015. The most influential factors were selected through the application of Gaussian Naive Bayes (GNB) and Adaboost machine learning algorithms, employing a Venn test for further refinement. Subsequently, six machine learning (ML) techniques, namely XGBoost, LightGBM, Random Forest (RF), Adaptive Boosting (Adaboost), Support Vector Machine (SVM), and Multilayer Perceptron (MLP), were employed to construct predictive models for distant metastasis. Shapley Additive Interpretation (SHAP) analysis facilitated a visual interpretation for individual patient. Model validity was assessed using accuracy, sensitivity, specificity, positive predictive value, negative predictive value, F1 score, and the area under the receiver operating characteristic curve (AUC).ResultsIn the realm of predicting distant metastasis, the Random Forest (RF) model outperformed the other five machine learning algorithms. The RF model demonstrated accuracy, sensitivity, specificity, positive predictive value, negative predictive value, F1 score, and AUC (95% CI) values of 0.792 (0.762–0.823), 0.904 (0.835–0.973), 0.759 (0.731–0.787), 0.221 (0.186–0.256), 0.974 (0.967–0.982), 0.353 (0.306–0.399), and 0.834 (0.696–0.967), respectively, surpassing the performance of other models. Additionally, the calibration curve's Brier Score (95%) for the RF model reached the minimum value of 0.06256 (0.05753–0.06759). SHAP analysis provided independent explanations, reaffirming the critical clinical factors associated with the risk of metastasis in OCCC patients.ConclusionsThis study successfully established a precise predictive model for OCCC patient metastasis using machine learning techniques, offering valuable support to clinicians in making informed clinical decisions.

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Section: Methodsmentioning

confidence: 99%