2023 World Conference on Lung Cancer (Posters)-EP17.01. Proposing Relevant Diagnosis Associated with Lung Cancer Based on Prior Diagnosis: Implementing Apriori Algorithm

EP17.01. Proposing Relevant Diagnosis Associated with Lung Cancer Based on Prior Diagnosis: Implementing Apriori Algorithm - PDF(Abstract)

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This study aimed to identify relevant diagnoses associated with lung cancer by applying the Apriori algorithm to a large dataset of prior diagnoses. The algorithm was used to analyze a dataset of 205,762 patients who visited a tertiary academic hospital and had 18,111,638 diagnoses recorded. The analysis revealed a total of 82,873 association rules.<br /><br />The results showed that lung cancer was associated with various diagnoses, including abnormal findings on lung imaging, chronic obstructive pulmonary disease, sleep disorders, other respiratory disorders, secondary malignant neoplasms of other or unspecified sites, pneumonia, respiratory conditions due to other external agents, respiratory failure, and emphysema.<br /><br />By using the Apriori algorithm, potential diagnoses for lung cancer patients can be identified, which may facilitate earlier detection of pre-symptomatic diseases. However, further research is needed to establish the causal and temporal relationships between these diagnoses.<br /><br />Overall, this study demonstrates the potential of data mining algorithms in predicting additional diagnoses based on prior diagnosis data. The findings could have implications for improving the diagnosis and treatment of lung cancer patients.

Asset Subtitle

So Yeon Kim

Meta Tag

Speaker So Yeon Kim

Topic Global Health, Health Services & Health Economics: Digital Solutions

Keywords

lung cancer

Apriori algorithm

diagnoses

dataset

association rules

abnormal findings

chronic obstructive pulmonary disease

sleep disorders

respiratory disorders

data mining algorithms