Development and validation of machine learning models to predict survival of patients with resected stage-III NSCLC

Jin, Long; Zhao, Qifan; Fu, Shihua; Cao, Fei; Hou, Bin; Ma, Jun

doi:10.3389/fonc.2023.1092478

Cited by 5 publications

(1 citation statement)

References 35 publications

(37 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…For simplicity and availability, we selected the following ML models: LR [95,96], Linear SVM [97][98][99], Decision Tree (DT) [100], RF [101][102][103], Extra Trees (ET) [104,105], Extreme Gradient Boost (XGB) [73,106], K-Nearest Neighbors (KNN) [107,108], Linear Discriminant Analysis (LDA) [109,110], Light Gradient Boosting Machine (LGBM) [111,112], and Naive Bayes (NB) [113]. We specifically chose six nonlinear models (DT, RF, ET, XGB, KNN, LGBM) as they are suitable for nonlinear classification tasks, which is crucial for effectively classifying binary-encoded miRNA species.…”

Section: Machine Learning Classifiersmentioning

confidence: 99%

GeneAI 3.0: Powerful, Novel, Generalized Hybrid and Ensemble Deep Learning Frameworks for miRNA Classification of species-specific Stationary Patterns from Nucleotides

Singh

Khanna

Rout

et al. 2023

Preprint

View full text Add to dashboard Cite

Background and Motivation: Due to the intricate relationship between the small non-coding ribonucleic acid (miRNA) sequences, the classification of miRNA species, namely Human, Gorilla, Rat, and Mouse is challenging. Previous methods are not robust and accurate. In this study, we present GeneAI 3.0 (AtheroPoint™, Roseville, CA, USA), a powerful, novel, and generalized method for extracting features from the fixed patterns of purines and pyrimidines in each miRNA sequence in ensemble paradigms in machine learning (EML) and convolutional neural network (CNN)-based deep learning (EDL) frameworks. Method: GeneAI 3.0 utilized five conventional (Entropy, Dissimilarity, Energy, Homogeneity, and Contrast), and three contemporary (Shannon entropy, Hurst exponent, Fractal dimension) features, to generate a compositefeature set from given miRNA sequences which were then passed into our ML and DL classification framework. A set of 11 new classifiers was designed consisting of five EML and six EDL for binary/multiclass classification. It was benchmarked against 9 solo ML (SML), 6 solo DL (SDL), 12 hybrid DL (HDL) models, resulting in a total of 11+27=38 models were designed. Four hypotheses were formulated and validated using explainable AI (XAI) as well as reliability/statistical tests. Results: The order of the mean performance using accuracy (ACC)/area-under-the-curve (AUC) of the 24 DL classifiers was: EDL>HDL>SDL. The mean performance of EDL models with CNN layers was superior to that without CNN layers by 0.73%/0.92%. Mean performance of EML models was superior to SML models with improvements of ACC/AUC by 6.24%/6.46%. EDL models performed significantly better than EML models, with a mean increase in ACC/AUC of 7.09%/6.96%. The GeneAI 3.0 tool produced expected XAI feature plots, and the statistical tests showed significant p-values. Conclusions: Ensemble models with composite features are highly effective and generalized models for effectively classifying miRNA sequences.

show abstract

Section: Machine Learning Classifiersmentioning

confidence: 99%

GeneAI 3.0: Powerful, Novel, Generalized Hybrid and Ensemble Deep Learning Frameworks for miRNA Classification of species-specific Stationary Patterns from Nucleotides

Singh

Khanna

Rout

et al. 2023

Preprint

View full text Add to dashboard Cite

show abstract

A Systematic Review of Artificial Intelligence Models for Time-to-Event Outcome Applied in Cardiovascular Disease Risk Prediction

Teshale,

Htun,

Vered

et al. 2024

J Med Syst

View full text Add to dashboard Cite

Artificial intelligence (AI) based predictive models for early detection of cardiovascular disease (CVD) risk are increasingly being utilised. However, AI based risk prediction models that account for right-censored data have been overlooked. This systematic review (PROSPERO protocol CRD42023492655) includes 33 studies that utilised machine learning (ML) and deep learning (DL) models for survival outcome in CVD prediction. We provided details on the employed ML and DL models, eXplainable AI (XAI) techniques, and type of included variables, with a focus on social determinants of health (SDoH) and gender-stratification. Approximately half of the studies were published in 2023 with the majority from the United States. Random Survival Forest (RSF), Survival Gradient Boosting models, and Penalised Cox models were the most frequently employed ML models. DeepSurv was the most frequently employed DL model. DL models were better at predicting CVD outcomes than ML models. Permutation-based feature importance and Shapley values were the most utilised XAI methods for explaining AI models. Moreover, only one in five studies performed gender-stratification analysis and very few incorporate the wide range of SDoH factors in their prediction model. In conclusion, the evidence indicates that RSF and DeepSurv models are currently the optimal models for predicting CVD outcomes. This study also highlights the better predictive ability of DL survival models, compared to ML models. Future research should ensure the appropriate interpretation of AI models, accounting for SDoH, and gender stratification, as gender plays a significant role in CVD occurrence.

show abstract

Predicting lung cancer survival based on clinical data using machine learning: A review

Altuhaifa

Win

2023

Computers in Biology and Medicine

View full text Add to dashboard Cite

Development and validation of machine learning models to predict survival of patients with resected stage-III NSCLC

Cited by 5 publications

References 35 publications

GeneAI 3.0: Powerful, Novel, Generalized Hybrid and Ensemble Deep Learning Frameworks for miRNA Classification of species-specific Stationary Patterns from Nucleotides

GeneAI 3.0: Powerful, Novel, Generalized Hybrid and Ensemble Deep Learning Frameworks for miRNA Classification of species-specific Stationary Patterns from Nucleotides

A Systematic Review of Artificial Intelligence Models for Time-to-Event Outcome Applied in Cardiovascular Disease Risk Prediction

Predicting lung cancer survival based on clinical data using machine learning: A review

Contact Info

Product

Resources

About