A comparative study on feature selection for a risk prediction model for colorectal cancer

Cueto-López, Nahúm; Garcí­a-Ordás, María Teresa; Dávila-Batista, Verónica; Aragonés, Núria; Aláiz-Rodríguez, Rocío

doi:10.1016/j.cmpb.2019.06.001

Cited by 46 publications

(29 citation statements)

References 37 publications

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“…elastic net regression) and machine learning algorithms (i.e. random survival forest) might offer additional means for model improvement [ 24 , 25 ]. Finally, model communication to the wider public was generally not addressed by previous studies and was restricted to providing a formula to calculate individual absolute risk of colorectal cancer [ 18 ].…”

Section: Introductionmentioning

confidence: 99%

Development and validation of a lifestyle-based model for colorectal cancer risk prediction: the LiFeCRC score

Aleksandrova

Reichmann

Kaaks

et al. 2021

BMC Med

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Background Nutrition and lifestyle have been long established as risk factors for colorectal cancer (CRC). Modifiable lifestyle behaviours bear potential to minimize long-term CRC risk; however, translation of lifestyle information into individualized CRC risk assessment has not been implemented. Lifestyle-based risk models may aid the identification of high-risk individuals, guide referral to screening and motivate behaviour change. We therefore developed and validated a lifestyle-based CRC risk prediction algorithm in an asymptomatic European population. Methods The model was based on data from 255,482 participants in the European Prospective Investigation into Cancer and Nutrition (EPIC) study aged 19 to 70 years who were free of cancer at study baseline (1992–2000) and were followed up to 31 September 2010. The model was validated in a sample comprising 74,403 participants selected among five EPIC centres. Over a median follow-up time of 15 years, there were 3645 and 981 colorectal cancer cases in the derivation and validation samples, respectively. Variable selection algorithms in Cox proportional hazard regression and random survival forest (RSF) were used to identify the best predictors among plausible predictor variables. Measures of discrimination and calibration were calculated in derivation and validation samples. To facilitate model communication, a nomogram and a web-based application were developed. Results The final selection model included age, waist circumference, height, smoking, alcohol consumption, physical activity, vegetables, dairy products, processed meat, and sugar and confectionary. The risk score demonstrated good discrimination overall and in sex-specific models. Harrell’s C-index was 0.710 in the derivation cohort and 0.714 in the validation cohort. The model was well calibrated and showed strong agreement between predicted and observed risk. Random survival forest analysis suggested high model robustness. Beyond age, lifestyle data led to improved model performance overall (continuous net reclassification improvement = 0.307 (95% CI 0.264–0.352)), and especially for young individuals below 45 years (continuous net reclassification improvement = 0.364 (95% CI 0.084–0.575)). Conclusions LiFeCRC score based on age and lifestyle data accurately identifies individuals at risk for incident colorectal cancer in European populations and could contribute to improved prevention through motivating lifestyle change at an individual level.

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Section: Introductionmentioning

confidence: 99%

Development and validation of a lifestyle-based model for colorectal cancer risk prediction: the LiFeCRC score

Aleksandrova

Reichmann

Kaaks

et al. 2021

BMC Med

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“…These scalar metrics can be seen as projections to one dimensional space and its use only shows where the feature selector stands in relation to the stable and the random ranking algorithm. If we change from a projection to a onedimensional space, into a space with two or more dimensions, we have a visual representation that allows to establish comparisons with respect to the random selector as well as comparisons of each feature selector to the others [60]. Conducting a visual-based stability analysis allows the evaluation of the similarity between feature ranking algorithms as well as their stability.…”

Section: A Stability Of Feature Selection Methodsmentioning

confidence: 99%

A Machine Learning Approach for Lamb Meat Quality Assessment Using FTIR Spectra

Aláiz-Rodríguez¹,

Parnell

2020

IEEE Access

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The food industry requires automatic methods to establish authenticity of food products. In this work, we address the problem of the certification of suckling lamb meat with respect to the rearing system. We evaluate the performance of neural network classifiers as well as different dimensionality reduction techniques, with the aim of categorizing lamb fat by means of spectroscopy and analyzing the features with more discrimination power. Assessing the stability of feature ranking algorithms also becomes particularly important. We assess six feature selection techniques (χ 2 , Information Gain, Gain Ratio, Relief and two embedded techniques based on the decision rule 1R and SVM (Support Vector Machine). Additionally, we compare them with common approaches in the chemometrics field like the Partial Least Square (PLS) model and Principal Component Analysis (PCA) regression. Experimental results with a fat sample dataset collected from carcasses of suckling lambs show that performing feature selection contributes to classification performance increasing accuracy from 89.70% with the full feature set to 91.80% and 93.89% with the SVM approach and PCA, respectively. Moreover, the neural classifiers yield a significant increase in the accuracy with respect to the PLS model (85.60% accuracy). It is noteworthy that unlike PCA or PLS, the feature selection techniques that select relevant wavelengths allow the user to identify the regions in the spectrum with the most discriminant power, which makes the understanding of this process easier for veterinary experts. The robustness of the feature selection methods is assessed via a visual approach. INDEX TERMS Meat quality assessment, feature selection, machine learning, neural networks, feature selection robustness.

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“…ANN models should be developed to help construct other ANN models for decision making. Although results showed that ANNs outperformed other AI methods, certain studies also highlighted their limitations[ 187 , 188 ]. An internal comparison between different ML methods is required.…”

Section: Features Limitations and Future Perspectivesmentioning

confidence: 99%

Status quo and future prospects of artificial neural network from the perspective of gastroenterologists

Cao¹,

Zhang²,

Wei³

et al. 2021

WJG

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Artificial neural networks (ANNs) are one of the primary types of artificial intelligence and have been rapidly developed and used in many fields. In recent years, there has been a sharp increase in research concerning ANNs in gastrointestinal (GI) diseases. This state-of-the-art technique exhibits excellent performance in diagnosis, prognostic prediction, and treatment. Competitions between ANNs and GI experts suggest that efficiency and accuracy might be compatible in virtue of technique advancements. However, the shortcomings of ANNs are not negligible and may induce alterations in many aspects of medical practice. In this review, we introduce basic knowledge about ANNs and summarize the current achievements of ANNs in GI diseases from the perspective of gastroenterologists. Existing limitations and future directions are also proposed to optimize ANN’s clinical potential. In consideration of barriers to interdisciplinary knowledge, sophisticated concepts are discussed using plain words and metaphors to make this review more easily understood by medical practitioners and the general public.

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A comparative study on feature selection for a risk prediction model for colorectal cancer

Cited by 46 publications

References 37 publications

Development and validation of a lifestyle-based model for colorectal cancer risk prediction: the LiFeCRC score

Development and validation of a lifestyle-based model for colorectal cancer risk prediction: the LiFeCRC score

A Machine Learning Approach for Lamb Meat Quality Assessment Using FTIR Spectra

Status quo and future prospects of artificial neural network from the perspective of gastroenterologists

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