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Prediction of esophageal cancer using demographic, lifestyle, patient history, and basic clinical tests. (English) Zbl 1390.92072
Summary: An early detection of a disease can save many lives but it is impractical to undergo all medical tests for many prevalent diseases. Further these tests are costly, painful, time consuming and may have side effects. We have tried to predict esophageal cancer using demographics, lifestyle, medical history information, and basic clinical tests initially and later removed all clinical tests one after another to study the change of the accuracy without these clinical tests. It is well studied that no single classification technique turns out to be best for all the problems. Here, we test Naive Bayes classification, random forests, support vector machines (SVM) and logistic regression (LR), which perform similarly when all tests are used. However, as we reduce the number of tests, naive versions of these classifiers perform worse than kernelized versions of SVM and LR. We test our methodology with electronic medical record (EMR) of 3500 patients (approx.). The four methods described above, demonstrate a high accuracy with all features, including basic clinical test and show very low accuracy without the basic clinical tests measured by medical practitioner (MP). LR with a polynomial feature transformation of degree three yields an accuracy of 100% (approx), even without the MP features. Further dropping clinical tests one after another we see a decline in the accuracy of detection to 96%. We have also observed high sensitivity to 100% which indicates that no real patients are undetected in this experiment.

MSC:
92C50 Medical applications (general)
62P10 Applications of statistics to biology and medical sciences; meta analysis
62-07 Data analysis (statistics) (MSC2010)
Software:
LIBLINEAR; LIBSVM; SMOTE; WEKA
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