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A relook at logistic regression methods for the initial detection of lung ailments using clinical data and chest radiography

Mohd. Noor, Norliza and Rijal, Omar Mohd. and Iqba, Mohd. and Yunus, Ashari (2009) A relook at logistic regression methods for the initial detection of lung ailments using clinical data and chest radiography. WSEAS Transactions on Information Science and Applications , 6 (9). pp. 1503-1508. ISSN 17900832

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Abstract

The problem of diagnosing patients with lung ailments such as Tuberculosis (PTB), Pneumonia (PNEU) and Lung Cancer (LC) when making their initial visit to a medical institution is the focus of this study. Clinical data involving symptoms and signs are used to make important decisions before the availability of the results of further tests. In practice, Logistic Regression Methods are frequently involved in this type of decision making. However, the problem of missing values when the numerical values of certain explanatory variables are not available persists in practical situations. In this paper a logistic regression model using four variables (age, cough, loss of weight (LOW) and loss of appetite (LOA)) are investigated for each of the three diseases. The main result of this study is that the probability of misclassifying the three disease type is large, and that good model fitting does not guarantee correct diagnosis. As a viable substitute, a graphical method of detection with an 85% chance of correct classification based on information extracted from the chest radiograph images is proposed.

Item Type:Article
Uncontrolled Keywords:chest radiography, clinical data, error probability, lung disease, missing values, statistical detection
Subjects:T Technology > TK Electrical engineering. Electronics Nuclear engineering
Divisions:Electrical Engineering
ID Code:12819
Deposited By: Ms Zalinda Shuratman
Deposited On:30 Jun 2011 10:12
Last Modified:30 Jun 2011 10:12

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