Mobile-Based Machine Learning Application for Early Detection of Lung Cancer
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New research compares nine classification algorithms for detecting lung cancer using the NusaMiner application. Using a dataset of 309 records from Kaggle, the study found that the Gradient Boosting and Random Forest algorithms achieved up to 100% accuracy. NusaMiner has the potential to improve early detection of lung cancer, offering an effective and efficient solution for healthcare services.
Mobile Machine Learning Application for Early Detection of Lung Cancer Shows Promising Results
A recent study evaluated the performance of nine classification algorithms in detecting lung cancer through the mobile application NusaMiner. The study utilized a dataset from Kaggle, consisting of 309 records with 16 attributes related to patient risk factors and symptoms.
Findings
Results indicated that NusaMiner achieved high and stable accuracy across different data split ratios. Notably, the algorithms Gradient Boosting and Random Forest emerged as top performers, with accuracy rates reaching 100% in several scenarios. This positions NusaMiner as a potentially innovative solution for enhancing early lung cancer detection.
Healthcare Implications
NusaMiner could significantly improve the speed and reliability of lung cancer diagnoses, potentially leading to better patient outcomes. Its high accuracy makes it a valuable tool for healthcare providers implementing early detection strategies.
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📰 Original Source: https://doi.org/10.37817/ikraith-informatika.v10i2.5474
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