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Computer‐aided disease prediction system: development of application software with SAS component language
Author(s) -
Chang ChiMing,
Kuo HsuSung,
Chang ShuHui,
Chang HongJen,
Liou DerMing,
Laszlo Tabar,
Chen Tony HsiuHsi
Publication year - 2005
Publication title -
journal of evaluation in clinical practice
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.737
H-Index - 73
eISSN - 1365-2753
pISSN - 1356-1294
DOI - 10.1111/j.1365-2753.2005.00514.x
Subject(s) - computer science , logistic regression , predictive modelling , disease , component (thermodynamics) , software , machine learning , medicine , data mining , artificial intelligence , pathology , physics , thermodynamics , programming language
Aims The intricacy of predictive models associated with prognosis and risk classification of disease often discourages medical personnel who are interested in this field. The aim of this study was therefore to develop a computer‐aided disease prediction model underpinning a step‐by‐step statistics‐guided approach including five components: (1) data management; (2) exploratory analysis; (3) type of predictive model; (4) model verification; (5) interactive mode of disease prediction using SAS 8.02 Windows 2000 as a platform. Methods The application of this system was illustrated by using data from the Swedish Two‐County Trial on breast cancer screening. The effects of tumour size, node status, and histological grade on breast cancer death using logistic regression model or survival models were predicted. A total of 20 questions were designed to exemplify the usefulness of each component. We also evaluated the system using a controlled randomized trial. Times to finish the above 20 questions were used as endpoint to evaluate the performance of the current system. User satisfaction with the current system such as easy to use, the efficiency of risk prediction, and the reduction of barrier to predictive model was also evaluated. Results The intervention group not only performed more efficiently than the control group but also satisfied with this application software. Conclusions The MD‐DP‐SOS system characterized by menu‐driven style, comprehensiveness, accuracy and adequacy assessment, and interactive mode of disease prediction is helpful for medical personnel who are involved in disease prediction.