Bayesian classifier| Volume 86, ISSUE 2, P211-216, February 2008

Download started.


Application of the Naïve Bayesian Classifier to optimize treatment decisions

Published:November 19, 2007DOI:


      Background and purpose

      To study the accuracy, specificity and sensitivity of the Naïve Bayesian Classifier (NBC) in the assessment of individual risk of cancer relapse or progression after radiotherapy (RT).

      Materials and methods

      Data of 142 brain tumour patients irradiated from 2000 to 2005 were analyzed. Ninety-six attributes related to disease, patient and treatment were chosen. Attributes in binary form consisted of the training set for NBC learning. NBC calculated an individual conditional probability of being assigned to: relapse or progression (1), or no relapse or progression (0) group. Accuracy, attribute selection and quality of classifier were determined by comparison with actual treatment results, leave-one-out and cross validation methods, respectively.
      Clinical setting test utilized data of 35 patients. Treatment results at classification were unknown and were compared with classification results after 3 months.


      High classification accuracy (84%), specificity (0.87) and sensitivity (0.80) were achieved, both for classifier training and in progressive clinical evaluation.


      NBC is a useful tool to support the assessment of individual risk of relapse or progression in patients diagnosed with brain tumour undergoing RT postoperatively.


      To read this article in full you will need to make a payment

      Purchase one-time access:

      Academic & Personal: 24 hour online accessCorporate R&D Professionals: 24 hour online access
      One-time access price info
      • For academic or personal research use, select 'Academic and Personal'
      • For corporate R&D use, select 'Corporate R&D Professionals'


      Subscribe to Radiotherapy and Oncology
      Already a print subscriber? Claim online access
      Already an online subscriber? Sign in
      Institutional Access: Sign in to ScienceDirect


        • Goodman S.N.
        Introduction to Bayesian methods: the strength of evidence.
        Clin Trials. 2005; 2: 282-290
        • Peter J.F.
        Lucas: model-based diagnosis in medicine.
        Artif Intell Med. 1997; 10: 201-208
        • Yan H.
        • Yin F.F.
        • Willet C.
        Evaluation of an artificial intelligence guided inverse planning system: clinical case study.
        Radiother Oncol. 2007; 83: 76-85
        • Ericsen J.G.
        • Buffa F.M.
        • Alsner J.
        • Steiniche T.
        • Bentzen S.M.
        • Overgard J.
        Molecular profiles as predictive marker for the effect of overall treatment time of radiotherapy in supraglottic larynx squamous cell carcinomas.
        Radiother Oncol. 2004; 72: 275-282
        • Walker M.D.
        • Alexander E.
        • Hunt W.E.
        • et al.
        Evaluation of BCNU and/or radiotherapy in the treatment of anaplastic gliomas.
        J Neurosurg. 1978; 49: 333-343
        • Kononenko I.
        Inductive and Bayesian learning in medical diagnosis.
        Appl Artif Intell. 1993; 7: 317-337
        • Coiera E.W.
        Artificial Intelligence in medicine: the challenges ahead.
        J Am Med Inform. 1996; 3: 363-366
        • Abu-Hanna A.
        • Lucas P.
        Prognostic models in medicine.
        Methods Inf Med. 2001; 40: 1-5
        • Kristiansen K.
        • Hagen S.
        • Kollevold T.
        • et al.
        Combined modality therapy of operated astrocytoma grade III and IV: confirmation of the value of postoperative irradiation and lack of potentiation of bleomycin on survival: a postoperative multicenter trial of the Scandinavian Glioblastoma Study Group.
        Cancer. 1981; 47: 649-652
        • Jeremic B.
        • Milicic B.
        • Grujicic D.
        • Dagovic A.
        • Aleksandrovic J.
        Multivariate analysis of clinical prognostic factors in patients with glioblastoma multiformae treated with a combined modality approach.
        J Cancer Res Clin Oncol. 2003; 129: 477-484
        • Curran W.J.
        • Scott C.B.
        • Horton J.
        • et al.
        Recursive partitioning analysis of prognostic factors in the Radiation Therapy Oncology Group malignant glioma trials.
        J Natl Cancer Inst. 1993; 85: 704-710
        • Van der Gaag L.C.
        • Abu-Hanna A.
        Bayesian networks in biomedicine and healthcare.
        Artif Intell Med. 2004; 30: 201-210
        • Eisenstein E.L.
        • Alemi F.
        An evaluation of factors influencing Bayesian learning systems.
        J Am Med Inform. 1994; 1: 272-284
      1. Jakulin A, Bratko I, Smrke D, Demsar J, Zupan B. Attribute interactions in medical data analysis. Proc 9th Conf Artif Intell Med Europe (AIME 2003), Protaras, Cyprus, October 18–22.

        • Buckner J.C.
        Factors influencing survival in high grade gliomas.
        Semin Oncol. 2003; : 10-14
        • Davies E.
        • Clarke C.
        Early symptoms of brain tumours.
        J Neur Neurosurg Psych. 2004; 75: 1205-1206
        • Fijuth J.
        Stereotactic radiotherapy for primary and recurrent brain tumours. A new method for improvement of the treatment results?.
        Reports Practic Oncol Radiother. 2001; 6: 56
        • Hess K.R.
        • Wong E.T.
        • Jaeckle K.A.
        • Kyrytsis A.P.
        • Levin V.A.
        • Prados M.D.
        • et al.
        Response and progression in recurrent malignant glioma.
        Neuro-Oncol. 1999; 1: 282-288
        • Jeremic B.
        • Milicic B.
        • Grujicic D.
        • Dagovic A.
        • Aleksandrovic J.
        • Nikoli N.
        Clinical prognostic factors in patients with malignant glioma treated with combined modality approach.
        Am J Clin Oncol. 2004; 27: 195-204
        • Lote K.
        • Egeland T.
        • Hager B.
        • Stenwig B.
        • Skullerud K.
        • Berg-Johnsen J.
        • et al.
        Survival, prognostic factors, and therapeutic efficacy in low grade glioma: a retrospective study in 379 patients.
        J Clin Oncol. 1997; 9: 3129-3140
        • Lutterbach J.
        • Sauerbrei W.
        • Guttenberger R.
        Multivariate analysis of prognostic factors in patient with glioblastoma.
        Strahlenther Onkol. 2003; 179: 423
        • Maldaum M.V.
        • Suki D.
        • Lang F.F.
        • Prabhu S.
        • Shi W.
        • Fuller G.N.
        Cystic glioblastoma multiformae: survival outcomes in 22 cases.
        J Neurosurg. 2004; 100: 61-67
        • Odrazka K.
        • Petera J.
        • Kohlova T.
        • Dolezel M.
        • Vaculikowa M.
        • Zouhar M.
        • et al.
        Prognostic impact of hemoglobin level prior to radiotherapy on survival in patients with glioblastoma.
        Strahlenter Oncol. 2003; 179: 615-619
        • Pluta E.
        • Gliński B.
        • Nowak-Sadzikowska J.
        Radiation therapy in the treatment of partially resected low grade cerebellar astrocytoma in adult patients.
        Reports Practic Oncol Radiother. 2001; 6: 47
        • Tortosa A.
        • Vinolas N.
        • Villa S.
        • Verger E.
        • Gil J.M.
        • Brell M.
        Prognostic implication of clinical, radiologic, and pathologic features in patients with anaplastic gliomas.
        Cancer. 2003; 97: 1063-1067
      2. Grąbczewski K. Zastosowanie kryterium separowalności do generowania reguł klasyfikacji na podstawie baz danych. PhD Thesis, Polish Academy of Science, 2003.

        • Blum A.I.
        • Langley P.
        Selection of relevant features and examples in machine learning.
        Artif Intell. 1997; 97: 245-271
        • Demsar J.
        • Zupan B.
        • Kattan M.V.
        • Beck J.R.
        • Bratko I.
        Naive Bayesian-based nomograms for prediction of prostate cancer recurrence.
        Stud Health Technol Inform. 1999; 68: 436-441
        • Van der Gaag L.C.
        • Renooij S.
        • Witteman C.L.M.
        • Aleman B.M.P.
        • Taal B.G.
        Probabilities for probabilistic network: a case study in oesophagal cancer.
        Artif Intell Med. 2002; 25: 123-148
        • Willoughby T.R.
        • Starkschall G.
        • Janjan N.A.
        • Rosen I.I.
        Evaluation and scoring of radiotherapy treatment plans using an artificial neural network.
        Int J Radiat Oncol Biol Phys. 1996; 34: 923-930
        • Wasyluk H.
        • Onisko A.
        • Druzdzel M.J.
        Support of diagnosis of liver disorders based on a causal Bayesian network model.
        Med Sci Monit. 2001; 7: 327-332