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논문 기본 정보
- 자료유형
- 학술대회자료
- 저자정보
- 저널정보
- 한국정보통신학회 INTERNATIONAL CONFERENCE ON FUTURE INFORMATION & COMMUNICATION ENGINEERING 2023 INTERNATIONAL CONFERENCE ON FUTURE INFORMATION & COMMUNICATION ENGINEERING Vo.14 No.1
- 발행연도
- 2023.1
- 수록면
- 43 - 48 (6page)
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Cancer is a fatal illness caused by a variety of pathological changes. Over time it becomes serious and life-threatening. It must be identified quickly in the initial stage and examined what could be beneficial for its cure. The primary cause of this disease is the growth of cancerous cells in a particular part of the body. Recent studies show that the increasing rate of casualty in both men and women is expanding rapidly. Once the cancerous cells are active in the body they multiply uncontrollably. Sometimes it cannot be prevented whereas, the risky factors can be reduced. So, the prediction of cancer is the most important task at its earliest stage for the survival of the patients. The detection of cancer can be done using machine learning algorithms like Decision Tree (DT), k-nearest neighbors (KNN) and XGBoost. The main objective of this paper is to diagnose cancer in its early stage by exploring the performance of classification algorithms. As part of our research, we looked at three machine learning models: Decision Tree (DT), k-Nearest Neighbors (KNN), XGBoost. One of the renowned hospitals in Pakistan, Regional Headquarter Hospital Shigar, provided the data for research purpose. Accuracy, specificity, sensitivity, and F1-score were used to gauge how well each of the three models performed. The accuracy of Decision Tree was 98.91%, followed by that of K-Nearest Neighbors and KNN, which were 97.83% and 95.30%, respectively.
목차
Abstract
Ⅰ. INTRODUCTION
Ⅱ. Materials AND METHODS
Ⅲ. RESULTS and Discussion
Ⅳ. CONCLUSIONS
REFERENCES
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