Classification of Lung Diseases Using the Desicison Tree Method

Sopiyan  Apandi; Nanang Nanang; Perani  Rosyani; Nuraina Nuraina; Saiyah  Awaliyah; Santi Ayu  Purnamawati; Vera  Oktaviani

doi:10.55927/fjst.v4i1.13442

Authors

Sopiyan Apandi Fakultas Ilmu Komputer, Departemen Teknik Informatika, Universitas Pamulang
Nanang Nanang Fakultas Ilmu Komputer, Departemen Teknik Informatika, Universitas Pamulang
Perani Rosyani Fakultas Ilmu Komputer, Departemen Teknik Informatika, Universitas Pamulang
Nuraina Nuraina Fakultas Ilmu Komputer, Departemen Teknik Informatika, Universitas Pamulang
Saiyah Awaliyah Fakultas Ilmu Komputer, Departemen Teknik Informatika, Universitas Pamulang
Santi Ayu Purnamawati Fakultas Ilmu Komputer, Departemen Teknik Informatika, Universitas Pamulang
Vera Oktaviani Fakultas Ilmu Komputer, Departemen Teknik Informatika, Universitas Pamulang

DOI:

https://doi.org/10.55927/fjst.v4i1.13442

Keywords:

Lung Cancer, Machine Learning, Decision Tree, Classification

Abstract

This research develops a machine learning-based classification method to detect lung cancer early, with the aim of increasing the life expectancy of patients. Lung cancer diagnosis generally requires manual interpretation of CT (Computed Tomography) images, which is prone to human error and time consuming. The proposed method is the Genetic K-Nearest Neighbor (GKNN) algorithm, which combines the advantages of genetic algorithms in parameter optimization with the K-Nearest Neighbor (KNN) approach for classification. The dataset used comes from Kaggle, includes 309 entries with 16 features, and has gone through pre-processing to minimize noise and improve data quality. The GKNN model was compared with other algorithms such as Support Vector Machine (SVM), Random Forest, Artificial Neural Network (ANN), and Decision Tree. Results show that Decision Tree achieves the highest accuracy of 98.39%, while GKNN offers a reliable solution with 90% accuracy and a low false positive rate. The results of this study are expected to be a reference in the development of artificial intelligence-based systems for medical applications, supporting faster and more accurate clinical decision-making.

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