Lung cancer EDA classification using the decision trees method in Python

Makkyah, Aqila Darin and Faisal, Muhammad ORCID: https://orcid.org/0000-0003-4884-7254 (2023) Lung cancer EDA classification using the decision trees method in Python. Informatics and Software Engineering, 1 (1). pp. 8-13. ISSN 2988-2818

Preview

Text 17887.pdf - Published Version Available under License Creative Commons Attribution Share Alike. Download (269kB) | Preview

Abstract

Cancer is the second leading cause of death worldwide. In Indonesia, it is one of the diseases with a high mortality rate. Most patients are unaware of their lung cancer condition, resulting in delayed treatment. A prediction method with high accuracy is needed for early detection of lung cancer. This study aims to classify lung cancer using the Decision Trees method and perform Exploratory Data Analysis (EDA) using a dataset obtained from Kaggle. The research achieved a high recall value for the positive class (Yes class) but a low recall for the negative class (No class). The study utilized the Decision Trees algorithm known for its good performance. The dataset used includes clinical and demographic information of patients. By building a Decision Trees model, the research successfully classified lung cancer with good accuracy. The EDA results also provided insights into important factors in lung cancer classification. This study has the potential to contribute to the development of predictive models for lung cancer.

Item Type:	Journal Article
Keywords:	classification; lung cancer; decision trees; explanatory data analysis
Subjects:	08 INFORMATION AND COMPUTING SCIENCES > 0801 Artificial Intelligence and Image Processing > 080109 Pattern Recognition and Data Mining
Divisions:	Faculty of Technology > Department of Informatics Engineering
Depositing User:	Muhammad Faisal
Date Deposited:	04 Jan 2024 14:59

Downloads

Origin of downloads

Actions (login required)

View Item