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Enhancing Asthma Diagnosis : Leveraging Machine Learning Algorithms for Improved Predictive Accuracy

원문정보

초록

영어
Asthma is an important global health problem affecting nearly 300 million individuals and responsible for 250,000 deaths each year. Asthma is defined by obstruction to the airways, and difficult testing of lung function via spirometry or body plethysmography require complete cooperation by patients in all populations, including the elderly and those who are otherwise ill. Knees even more problematic is that smoking tobacco or respiratory changes in patients with asthma are only largely detectable when it is too late, meaning their respiratory performance has been compromised.

목차

Abstract
I. INTRODUCTION
II. LITERATURE REVIEW
III. METHODOLOGY
A. DataSet
B. ML Models:
IV. SIMULATION AND RESULT
V. CONCLUSION AND FUTURE WORK
REFERENCES

저자

  • Sagheer Abbas [ Department of Computer Science, Prince Mohammad Bin Fahd University Alkhobar, Saudia Arabia ]
  • Hassan Faisal [ School of Computer Science National College of Business Administration and Economics, Lahore 54000, Pakistan ]
  • Armughan Amir [ School of Computer Science National College of Business Administration and Economics, Lahore 54000, Pakistan ]
  • Muntaha Liaqat [ School of Computer Science National College of Business Administration and Economics, Lahore 54000, Pakistan ]
  • Shahbaz Khurram [ School of Computer Science National College of Business Administration and Economics, Lahore 54000, Pakistan ]
  • Ubaid Ullah [ Faculty of Information and Communication Technology Universiti Tunku Abdul Rehman Malaysia ]

참고문헌

자료제공 : 네이버학술정보

    간행물 정보

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