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A predictive model for cerebrovascular disease using data mining

A predictive model for cerebrovascular disease using data mining. Presentation by: Swapna Savvana, Graduate Student, Institute of Technology, University of Washington, Tacoma. Authors : Duen-Yian Yeh Ching-Hsue Cheng Yen-Wen Chen . Agenda. Introduction and Motivation

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A predictive model for cerebrovascular disease using data mining

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  1. A predictive model for cerebrovascular disease using data mining Presentation by: Swapna Savvana, Graduate Student, Institute of Technology, University of Washington, Tacoma Authors: Duen-Yian Yeh Ching-Hsue Cheng Yen-Wen Chen

  2. Agenda • Introduction and Motivation • Cerebrovascular disease Features • Major diseases related with cerebrovascular disease • Data Set, Data Mining Classification Techniques • Data Mining procedure • Comparison of classification Models • Diagnosis rules • Conclusion

  3. Introduction and Motivation • Cerebrovascular disease is a type of pathological change in brain blood vessels and a general artery sclerosis complication • Cerebrovascular disease ranks 2 out of 10 death causes in Taiwan • High Medical Expenses, sometimes costs life • Pathogenesis of cerebrovascular disease is complex and variable • Accurate diagnosis in advance is difficult • Predictive model enhances the preventive medicine diagnosis

  4. Cerebrovascular disease Features • High prevalence • High fatality rate • High disability rate • High recurrence rate

  5. Major diseases related with cerebrovascular disease • Diabetes Mellitus(DM) • Hypertension (hp(b)) • Myocardial infarction (mi(H)) • Cardiogenic shock (car(H)) • Hyperlipaemia(lip(B)) • Arrhythmi (aarr(H)) • Ischemic heart disease hd(H) • Body mass index(bmi)

  6. Data set • 493 samples • Physical exam results • Blood test results • Diagnosis data Data Mining Classification Techniques • Decision Tree (C4.5 algorithm is used ) • Bayesian Classifier • Back propagation Neural network(BPNN)

  7. Data Mining procedure • Data collection and variable screening • Attribute Symbolization(N,CD,BH,DM,SM class codes) • Input Data Splitting (T1, T2, T3) • Classification algorithms used to construct the models • Classification efficiency analyses and comparison • Sensitivity: the probability of positive test given that the patient is ill (Mii/Mri) • Accuracy: the no of correctly classified instances percentage [(M11+M22+M33+M44+M55)/M] • Extraction of diagnosis classification rules

  8. Comparison of classification Models Diagnosis Rules

  9. Conclusion • Decision Tree has high accuracy and sensitivity • 16 Diagnosis rules are accurate

  10. Thank You!

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