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Poster Session Ⅱ : Artificial Intelligence / IoT & Big Data

Data Mining Techniques : More Accurate Classified Algorithm For Cardiopulmonary Diseases Prediction

첫 페이지 보기
  • 발행기관
    한국차세대컴퓨팅학회 바로가기
  • 간행물
    한국차세대컴퓨팅학회 학술대회 바로가기
  • 통권
    The 8th International Conference on Next Generation Computing 2022 (2022.10)바로가기
  • 페이지
    pp.245-248
  • 저자
    Taher M. Ghazal, Syed Hakim Masood, Atif Ali, Muhammad Usama Nazir
  • 언어
    영어(ENG)
  • URL
    https://www.earticle.net/Article/A419788

원문정보

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

키워드

Classified Algorithms Ambulatory Blood Pressure (ABP) Bagging Normotensive Logistic Model Tree Naive Bayes Tree Classification Prehypertensive Hypertensive.

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 ]

참고문헌

자료제공 : 네이버학술정보

간행물 정보

발행기관

  • 발행기관명
    한국차세대컴퓨팅학회 [Korean Institute of Next Generation Computing]
  • 설립연도
    2005
  • 분야
    공학>컴퓨터학
  • 소개
    본 학회는 차세대 PC 및 그 관련분야의 학술활동을 통하여 차세대 PC의 학문 및 기술발전을 도모하고 산업발전 및 국제협력 증진을 목적으로 한다.

간행물

  • 간행물명
    한국차세대컴퓨팅학회 학술대회
  • 간기
    반년간
  • 수록기간
    2021~2025
  • 십진분류
    KDC 566 DDC 004

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