메뉴 건너뛰기




Volumn 28, Issue 20, 2015, Pages 469-474

Machine learning for predictive modelling based on small data in biomedical engineering

Author keywords

Biomedical systems; Decision tree; Machine learning; Neural network; Small data

Indexed keywords

BIOMEDICAL ENGINEERING; CLASSIFICATION (OF INFORMATION); DECISION TREES; LEARNING SYSTEMS; MACHINE LEARNING; NEURAL NETWORKS; TREES (MATHEMATICS);

EID: 84992509174     PISSN: None     EISSN: 24058963     Source Type: Conference Proceeding    
DOI: 10.1016/j.ifacol.2015.10.185     Document Type: Conference Paper
Times cited : (91)

References (39)
  • 1
    • 84874221936 scopus 로고    scopus 로고
    • Artificial neural networks in medical diagnosis
    • Amato, F. et al., 2013. Artificial neural networks in medical diagnosis. Journal of Applied Biomedicine, 11, pp.47-58.
    • (2013) Journal of Applied Biomedicine , vol.11 , pp. 47-58
    • Amato, F.1
  • 2
    • 84887478929 scopus 로고    scopus 로고
    • Decision tree classifiers for automated medical diagnosis
    • Azar, A. & El-Metwally, S., 2013. Decision tree classifiers for automated medical diagnosis. Neural Computing and Applications, 23(7-8), pp.2387-2403.
    • (2013) Neural Computing and Applications , vol.23 , Issue.7-8 , pp. 2387-2403
    • Azar, A.1    El-Metwally, S.2
  • 3
    • 80455158268 scopus 로고    scopus 로고
    • Statistical learning methods as a preprocessing step for survival analysis: Evaluation of concept using lung cancer data
    • Behera, M. et al., 2011. Statistical learning methods as a preprocessing step for survival analysis: evaluation of concept using lung cancer data. BioMedical Engineering OnLine, 10(1), pp.10-97.
    • (2011) BioMedical Engineering OnLine , vol.10 , Issue.1 , pp. 10-97
    • Behera, M.1
  • 4
    • 11244346676 scopus 로고    scopus 로고
    • Prediction of the strength and fracture location of the femoral neck by CT-based finite-element method: A preliminary study on patients with hip fracture
    • Bessho, M. et al., 2004. Prediction of the strength and fracture location of the femoral neck by CT-based finite-element method: A preliminary study on patients with hip fracture. Journal of Orthopaedic Science, 9, pp.545-550.
    • (2004) Journal of Orthopaedic Science , vol.9 , pp. 545-550
    • Bessho, M.1
  • 5
    • 84861556816 scopus 로고    scopus 로고
    • Reporting and methods in clinical prediction research: A systematic review
    • Bouwmeester, W. et al., 2012. Reporting and Methods in Clinical Prediction Research: A Systematic Review. PLoS Med, 9(5), p.e1001221.
    • (2012) PLoS Med , vol.9 , Issue.5 , pp. e1001221
    • Bouwmeester, W.1
  • 7
    • 85009072850 scopus 로고    scopus 로고
    • Machine learning methodology in bioinformatics
    • N. Kasabov, ed. Springer Berlin Heidelberg
    • Campbell, C., 2014. Machine Learning Methodology in Bioinformatics. In N. Kasabov, ed. Springer Handbook of Bio-/Neuroinformatics. Springer Berlin Heidelberg, pp. 185-206.
    • (2014) Springer Handbook of Bio-/Neuroinformatics , pp. 185-206
    • Campbell, C.1
  • 9
    • 0035810969 scopus 로고    scopus 로고
    • Artificial neural network-based method of screening heart murmurs in children
    • DeGroff, C.G. et al., 2001. Artificial neural network-based method of screening heart murmurs in children. Circulation, 103, pp.2711-2716.
    • (2001) Circulation , vol.103 , pp. 2711-2716
    • DeGroff, C.G.1
  • 10
    • 78650202814 scopus 로고    scopus 로고
    • Epidemiology of hip fracture: Worldwide geographic variation
    • Dhanwal, D.K. et al., 2011. Epidemiology of hip fracture: Worldwide geographic variation. Indian Journal of Orthopaedics, 45(1), pp.15-22.
    • (2011) Indian Journal of Orthopaedics , vol.45 , Issue.1 , pp. 15-22
    • Dhanwal, D.K.1
  • 11
    • 84905981145 scopus 로고    scopus 로고
    • Comparison of the performance of log-logistic regression and artificial neural networks for predicting breast cancer relapse
    • Faradmal, J. et al., 2014. Comparison of the performance of log-logistic regression and artificial neural networks for predicting breast cancer relapse. Asian Pacific journal of cancer prevention : APJCP, 15(14), pp.5883-5888.
    • (2014) Asian Pacific Journal of Cancer Prevention : APJCP , vol.15 , Issue.14 , pp. 5883-5888
    • Faradmal, J.1
  • 12
    • 25444465350 scopus 로고    scopus 로고
    • Learning from little: Comparison of classifiers given little training
    • Forman, G. & Cohen, I., 2004. Learning from Little: Comparison of Classifiers Given Little Training. Proc PKDD, 19, pp.161-172.
    • (2004) Proc PKDD , vol.19 , pp. 161-172
    • Forman, G.1    Cohen, I.2
  • 13
    • 79951551258 scopus 로고    scopus 로고
    • Estimation of prediction error by using K-fold cross-validation
    • Fushiki, T., 2009. Estimation of prediction error by using K-fold cross-validation. Statistics and Computing, 21, pp.137-146.
    • (2009) Statistics and Computing , vol.21 , pp. 137-146
    • Fushiki, T.1
  • 14
    • 79952405336 scopus 로고    scopus 로고
    • Using neural network as a screening and educational tool for abnormal glucose tolerance in the community
    • Gao, W. et al., 2011. Using neural network as a screening and educational tool for abnormal glucose tolerance in the community. Archives of Public Health, 68(4), pp.143-154.
    • (2011) Archives of Public Health , vol.68 , Issue.4 , pp. 143-154
    • Gao, W.1
  • 17
    • 38749149232 scopus 로고    scopus 로고
    • Mathematical relationships between bone density and mechanical properties: A literature review
    • Helgason, B. et al., 2008. Mathematical relationships between bone density and mechanical properties: A literature review. Clinical Biomechanics, 23, pp.135-146.
    • (2008) Clinical Biomechanics , vol.23 , pp. 135-146
    • Helgason, B.1
  • 18
    • 46249092236 scopus 로고    scopus 로고
    • Testing a neural coding hypothesis using surrogate data
    • Hirata, Y. et al., 2008. Testing a neural coding hypothesis using surrogate data. Journal of Neuroscience Methods, 172, pp.312-322.
    • (2008) Journal of Neuroscience Methods , vol.172 , pp. 312-322
    • Hirata, Y.1
  • 19
    • 44649090674 scopus 로고    scopus 로고
    • Gene prediction in metagenomic fragments: A large scale machine learning approach
    • Hoff, K. et al., 2008. Gene prediction in metagenomic fragments: A large scale machine learning approach. BMC Bioinformatics, 9(1), pp.9-217.
    • (2008) BMC Bioinformatics , vol.9 , Issue.1 , pp. 9-217
    • Hoff, K.1
  • 20
    • 84868702962 scopus 로고    scopus 로고
    • Decision tree-based learning to predict patient controlled analgesia consumption and readjustment
    • Hu, Y.-J. et al., 2012. Decision tree-based learning to predict patient controlled analgesia consumption and readjustment. BMC Medical Informatics and Decision Making, 12(1), pp.12-131.
    • (2012) BMC Medical Informatics and Decision Making , vol.12 , Issue.1 , pp. 12-131
    • Hu, Y.-J.1
  • 22
    • 84992516917 scopus 로고    scopus 로고
    • An indispensable tool in bioinformatics
    • R. Matthiesen, ed. Humana Press
    • Machine Learning: An Indispensable Tool in Bioinformatics. In R. Matthiesen, ed. Bioinformatics Methods in Clinical Research. Humana Press, pp. 25-48.
    • Bioinformatics Methods in Clinical Research , pp. 25-48
    • Learning, M.1
  • 23
    • 0026438801 scopus 로고
    • The apparent incidence of hip fracture in Europe: A study of national register sources
    • Johnel, O. et al., 1992. The apparent incidence of hip fracture in Europe: A study of national register sources. Osteoporosis International, 2(6), pp.298-302.
    • (1992) Osteoporosis International , vol.2 , Issue.6 , pp. 298-302
    • Johnel, O.1
  • 24
    • 0344157373 scopus 로고    scopus 로고
    • Prediction of femoral fracture load using automated finite element modeling
    • Keyak, J.H. et al., 1997. Prediction of femoral fracture load using automated finite element modeling. Journal of Biomechanics, 31, pp.125-133.
    • (1997) Journal of Biomechanics , vol.31 , pp. 125-133
    • Keyak, J.H.1
  • 27
    • 0033544441 scopus 로고    scopus 로고
    • Process modeling with neural networks using small experimental datasets
    • Lanouette, R., Thibault, J. & Valade, J.L., 1999. Process modeling with neural networks using small experimental datasets. Computers & Chemical Engineering, 23(9), pp.1167-1176.
    • (1999) Computers & Chemical Engineering , vol.23 , Issue.9 , pp. 1167-1176
    • Lanouette, R.1    Thibault, J.2    Valade, J.L.3
  • 28
    • 84880335873 scopus 로고    scopus 로고
    • Using a multi-staged strategy based on machine learning and mathematical modeling to predict genotype-phenotype risk patterns in diabetic kidney disease: A prospective case-control cohort analysis
    • Leung, R. et al., 2013. Using a multi-staged strategy based on machine learning and mathematical modeling to predict genotype-phenotype risk patterns in diabetic kidney disease: a prospective case-control cohort analysis. BMC Nephrology, 14(1), pp.14-162.
    • (2013) BMC Nephrology , vol.14 , Issue.1 , pp. 14-162
    • Leung, R.1
  • 29
    • 84876336190 scopus 로고    scopus 로고
    • Significant IgG subclass heterogeneity in HLA-specific antibodies: Implications for pathogenicity, prognosis, and the rejection response
    • Lowe, D. et al., 2013. Significant IgG subclass heterogeneity in HLA-specific antibodies: Implications for pathogenicity, prognosis, and the rejection response. Human Immunology, 74, pp.666-672.
    • (2013) Human Immunology , vol.74 , pp. 666-672
    • Lowe, D.1
  • 30
    • 0001362410 scopus 로고
    • The Levenberg-Marquardt algorithm: Implementation and theory
    • More, J.J., 1978. The Levenberg-Marquardt algorithm: Implementation and theory. Lecture Notes in Mathematics, 630, pp.105-116.
    • (1978) Lecture Notes in Mathematics , vol.630 , pp. 105-116
    • More, J.J.1
  • 31
    • 0025536870 scopus 로고
    • Improving the learning speed of 2-layer neural networks by choosing initial values of the adaptive weights
    • Nguyen, D. & Widrow, B., 1990. Improving the learning speed of 2-layer neural networks by choosing initial values of the adaptive weights. IEEE International Joint Conference on Neural Networks, 3, pp.21-26.
    • (1990) IEEE International Joint Conference on Neural Networks , vol.3 , pp. 21-26
    • Nguyen, D.1    Widrow, B.2
  • 32
    • 35348846442 scopus 로고    scopus 로고
    • Structural parameters and mechanical strength of cancellous bone in the femoral head in osteoarthritis do not depend on age
    • Perilli, E. et al., 2007. Structural parameters and mechanical strength of cancellous bone in the femoral head in osteoarthritis do not depend on age. Bone, 41, pp.760-768.
    • (2007) Bone , vol.41 , pp. 760-768
    • Perilli, E.1
  • 33
    • 0036778479 scopus 로고    scopus 로고
    • Decision trees: An overview and their use in medicine
    • Podgorelec, V. et al., 2002. Decision trees: An overview and their use in medicine. Journal of Medical Systems, 26, pp.445-463.
    • (2002) Journal of Medical Systems , vol.26 , pp. 445-463
    • Podgorelec, V.1
  • 34
    • 84906877423 scopus 로고    scopus 로고
    • Artificial neural networks in hard tissue engineering: Another look at age-dependence of trabecular bone properties in osteoarthritis
    • Valencia: IEEE
    • Shaikhina, T., Khovanova, N. & Mallick, K., 2014. Artificial Neural Networks in Hard Tissue Engineering: Another Look at Age-Dependence of Trabecular Bone Properties in Osteoarthritis. In IEEE EMBS International Conference on Biomedical & Health Informatics. Valencia: IEEE, pp.484-487.
    • (2014) IEEE EMBS International Conference on Biomedical & Health Informatics , pp. 484-487
    • Shaikhina, T.1    Khovanova, N.2    Mallick, K.3
  • 35
    • 84855551703 scopus 로고    scopus 로고
    • Osteoarthritis : Diagnosis and treatment
    • Sinusas, K., 2012. Osteoarthritis : diagnosis and treatment. American Family Physician, 85(1), pp.49-56.
    • (2012) American Family Physician , vol.85 , Issue.1 , pp. 49-56
    • Sinusas, K.1
  • 36
    • 44049111332 scopus 로고
    • Testing for nonlinearity in time series: The method of surrogate data
    • Theiler, J. et al., 1992. Testing for nonlinearity in time series: the method of surrogate data. Physica D: Nonlinear Phenomena, 58, pp.77-94.
    • (1992) Physica D: Nonlinear Phenomena , vol.58 , pp. 77-94
    • Theiler, J.1
  • 37
    • 84880110259 scopus 로고    scopus 로고
    • Hip fracture risk assessment: Artificial neural network outperforms conditional logistic regression in an age- and sex-matched case control study
    • Tseng, W.-J. et al., 2013. Hip fracture risk assessment: artificial neural network outperforms conditional logistic regression in an age- and sex-matched case control study. BMC Musculoskeletal Disorders, 14(1), pp.14-207.
    • (2013) BMC Musculoskeletal Disorders , vol.14 , Issue.1 , pp. 14-207
    • Tseng, W.-J.1
  • 38
  • 39
    • 84905981311 scopus 로고    scopus 로고
    • Comparison between artificial neural network and Cox regression model in predicting the survival rate of gastric cancer patients
    • Zhu, L. et al., 2013. Comparison between artificial neural network and Cox regression model in predicting the survival rate of gastric cancer patients. Biomedical reports, 1(5), pp.757-760.
    • (2013) Biomedical Reports , vol.1 , Issue.5 , pp. 757-760
    • Zhu, L.1


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.