title: Fingerprint verification prediction model in hand dermatitis.
abstract: BACKGROUND
  Hand dermatitis associated fingerprint changes is a significant problem and affects fingerprint verification processes. This study was done to develop a clinically useful prediction model for fingerprint verification in patients with hand dermatitis.
  
  
  METHODS
  A case-control study involving 100 patients with hand dermatitis. All patients verified their thumbprints against their identity card. Registered fingerprints were randomized into a model derivation and model validation group. Predictive model was derived using multiple logistic regression. Validation was done using the goodness-of-fit test.
  
  
  RESULTS
  The fingerprint verification prediction model consists of a major criterion (fingerprint dystrophy area of ≥ 25%) and two minor criteria (long horizontal lines and long vertical lines). The presence of the major criterion predicts it will almost always fail verification, while presence of both minor criteria and presence of one minor criterion predict high and low risk of fingerprint verification failure, respectively. When none of the criteria are met, the fingerprint almost always passes the verification. The area under the receiver operating characteristic curve was 0.937, and the goodness-of-fit test showed agreement between the observed and expected number (P = 0.26).
  
  
  CONCLUSIONS
  The derived fingerprint verification failure prediction model is validated and highly discriminatory in predicting risk of fingerprint verification in patients with hand dermatitis.
authors.affiliation: Faculty of Medicine and Health Science, UCSI University, Kuala Lumpur, Malaysia.
authors.forename: Chew K
authors.initials: CK
authors.lastname: Lee
authors.affiliation: Faculty of Medicine and Health Science, UCSI University, Kuala Lumpur, Malaysia.
authors.forename: Choong C
authors.initials: CC
authors.lastname: Chang
authors.affiliation: Faculty of Medicine and Health Science, UCSI University, Kuala Lumpur, Malaysia.
authors.forename: Asmah
authors.initials: A
authors.lastname: Johor
authors.affiliation: Faculty of Medicine and Health Science, UCSI University, Kuala Lumpur, Malaysia.
authors.forename: Puwira
authors.initials: P
authors.lastname: Othman
authors.affiliation: Faculty of Medicine and Health Science, UCSI University, Kuala Lumpur, Malaysia.
authors.forename: Roshidah
authors.initials: R
authors.lastname: Baba
mesh_terms: D000328:Adult; D019540:Area Under Curve; D016022:Case-Control Studies; D003878:Dermatoglyphics; D005260:Female; D006229:Hand Dermatoses / Q000150:complications / Q000473:pathology*; D006801:Humans; D008297:Male; D008875:Middle Aged; D015233:Models, Statistical; D015999:Multivariate Analysis; D010363:Pattern Recognition, Automated; D012372:ROC Curve; D011897:Random Allocation; D013933:Thumb