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Oral Cancer Prediction Model for Malaysian Sample

Issue: Vol.7, No.4 - October 2008

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Article Type: Manuscript

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  1. Dr Rosma Mohd Dom
    MARA University of Technology
  2. Sameem Abdul Kareem
  3. Basir Abidin
  4. Raja Latifah Raja Jallaludin
  5. Sok Ching Cheong
  6. Rosnah Binti Zain

Artificial intelligence prediction provides an alternative to existing statistical prediction models. In this paper the prediction of oral cancer susceptibility as a function of demographic profiles (age, gender, ethnicity), risk habits (cigarette smoking, alcohol drinking, tobacco chewing) and genetic markers (GSTM, GSTT1) were done using statistical logistic regression and fuzzy regression models on a sample of 84 oral cancer patients and 87 controls. Descriptive analysis including the odds ratios of predictor variables is described. "Optimal" input predictor set was determined based on standard variable selection techniques. Prediction performance and interpretation of the prediction equations generated by both regression models are analyzed.

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