Earticle

초록

영어

Data mining techniques develop a more accurate classification algorithm for patients classified as either normotensive, prehypertensive, or hypertensive. Logistic Model Tree, NBTree, and Bagging were chosen as the three classification models with tenfold cross-validation (LMT). Over 24 hours, we collected ABP readings from 1161 patients. To analyze the data, data mining techniques were used and a tool called WEKA. The data was analyzed based on age, gender, wake-up blood pressure, medication, sleep-up blood pressure, and overall blood pressure. According to bagging results, 886 cases (76.3 percent) are correctly classified, with 270 cases classified as pre-hypertensive, 436 cases as Normotensive, and 180 cases as hypertensive. NBTree's results show that 882 (75.9%) of the 1161 instances are correctly classified. Pre-hypertensive patients make up 256, normotensive patients 442, and hypertensive patients 184. Of the 1161 instances, the LMT algorithm correctly classified 878 (75.6 percent). According to the results, 275 people are pre-hypertensive, 431 are normotensive, and 172 are hypertensive. According to our findings, bagging is the most accurate classifier for the 24 hour ABP Monitoring dataset we used. Bagging achieves less overfitting because it focuses on global accuracy. It stabilizes and improves the accuracy of unstable methods compared to single classifiers.

목차

Abstract
I. INTRODUCTION
A. Blood Pressure in the Ambulatory System (ABP)
B. ABP's Importance
C. This Study's Importance
D. Techniques for Data Mining
II. LITERATURE REVIEW
III. EXPERIMENTAL STUDY
A. WEKA
B. Dataset
IV. PREPARATION OF DATA
V. MODELS FOR DATA MINING
A. Bagging
B. NBTree
C. Logistic Model Trees (LMT)
VI. EVALUATION MEASURE
A. Bagging
B. NBTree
C. Logistic Model Tree (LMT)
VII. RESULTS
A. Bagging
B. NBTree
C. Logistic Model Tree (LMT)
VIII. CONCLUSION
REFERENCES

저자

• Taher M. Ghazal [ School of Information Technology, Skyline University College, University City Sharjah, 1797, Sharjah, United Arab Emirates. ]
• Syed Hakim Masood [ University of Karachi, Pakistan ]
• Atif Ali [ UIIT, PMAS Arid Agriculture University Rawalpindi, Pakistan ]
• Muhammad Usama Nazir [ University of Central Punjab, Lahore, Pakistan ]

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