Comparison of the Performance of Log-logistic Regression and Artificial Neural Networks for Predicting Breast Cancer Relapse

Faradmal, Javad; Soltanian, Ali Reza; Roshanaei, Ghodratollah; Khodabakhshi, Reza; Kasaeian, Amir

doi:10.7314/apjcp.2014.15.14.5883

Cited by 28 publications

(25 citation statements)

References 22 publications

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“…It has been argued that ML can offer an indispensable tool for biomedical problems involving complex heterogeneous data when conventional statistical tools fail (Inza et al 2010;Campbell 2014;Grossi 2011). In applications such as gene selection (Hoff et al 2008), screening heart murmurs in children (DeGroff et al 2001), and predicting breast cancer relapse (Faradmal et al 2014), ML-based models were able to map highly non-linear input and output patterns even when mechanistic relationships between model variables could not be determined due to pathologies or complexity.…”

Section: Introductionmentioning

confidence: 99%

Machine Learning for Predictive Modelling based on Small Data in Biomedical Engineering

et al. 2015

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Experimental datasets in bioengineering are commonly limited in size, thus rendering Machine Learning (ML) impractical for predictive modelling. Novel techniques of multiple runs for model development and surrogate data analysis for model validation are suggested for prediction of biomedical outcomes based on small datasets for classification and regression tasks. The proposed framework was applied to designing a Neural Network model for osteoarthritic bone fracture risk stratification, and a Decision Tree model for prediction of antibody-mediated kidney transplant rejection. Despite the small datasets (35 bone specimens and 80 kidney transplants), the two models achieved high accuracy of 98.3% and 85%, respectively.

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Section: Introductionmentioning

confidence: 99%

Machine Learning for Predictive Modelling based on Small Data in Biomedical Engineering

et al. 2015

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“…Addition to the mentioned factors, all the MoBRPs recognized the significant effects for 'Tumor Size'; since the adverse effect of this factor was previously certified in many clinical researches (Faradmal, Soltanian, Roshanaei, Khodabakhshi, & Kasaeian, 2014); therefore, MoBRPs seems to be more able for risk factor recognition.…”

Section: Discussionmentioning

confidence: 94%

A Comparative Study for Modelling the Survival of Breast Cancer Patients in the West of Iran

Safe¹,

Mahjub²,

Faradmal³

2016

GJHS

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Background: Breast cancer is the main cause of women cancer mortality. Therefore, precise prediction of patients' risk level is the major concern in therapeutic strategies. Although statistical learning algorithms are high quality risk prediction methods, but usually their better prediction quality leads to more loss of interpretability. Therefore, the aim of this study is to compare 'Model-Based Recursive Partitioning' and 'Random Survival Forest'; whether the partitioning, as the more interpretable learning technique, could be a suitable successor for forests.

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“…Also Cox is not able to model the nonlinear relations. Some other models such as artificial neural networks (ANN) and support vector machines (SVM) are applied for overcoming these problems [4][5][6]. SVM models are based on the statistical learning theory and have some beneficial features.…”

Section: Introductionmentioning

confidence: 99%

Performance Evaluation of Support Vector Regression Models for Survival Analysis: A Simulation Study

Mahjub¹,

Goli²,

Faradmal³

et al. 2016

ijacsa

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Abstract-Desirable features of support vector regression (SVR) models have led to researchers extending them to survival problems. In current paper we evaluate and compare performance of different SVR models and the Cox model using simulated and real data sets with different characteristics. Several SVR models are applied: 1) SVR with only regression constraints (standard SVR); 2) SVR with regression and ranking constraints; 3) SVR with positivity constraints; and 4) L1-SVR. Also, a SVR model based on mean residual life is proposed.Our findings from evaluation of real data sets indicate that for data sets with high censoring rate and high number of features, SVR model significantly outperforms the Cox model. Simulated data sets also show similar results. For some real data sets L1-SVR has a significantly degraded performance in comparison to the standard SVR. Performance of other SVR models is not substantially different from the standard SVR with the real data sets. Nevertheless, the results of simulated data sets show that standard SVR slightly outperforms SVR with regression and ranking constraints.

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Comparison of the Performance of Log-logistic Regression and Artificial Neural Networks for Predicting Breast Cancer Relapse

Cited by 28 publications

References 22 publications

Machine Learning for Predictive Modelling based on Small Data in Biomedical Engineering

Machine Learning for Predictive Modelling based on Small Data in Biomedical Engineering

A Comparative Study for Modelling the Survival of Breast Cancer Patients in the West of Iran

Performance Evaluation of Support Vector Regression Models for Survival Analysis: A Simulation Study

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