Chemotherapy: Open Access
Open Access

Abstract

A Novel K Nearest Neighbor Classifier for Lung Cancer Disease Diagnosis

Ravi Kumar Sachdeva, Priyanka Bathla, Pooja Rani, Rohit Lamba, G.S. Pradeep Ghantasala, Ibrahim F. Nassar*

One of the world's deadliest diseases is lung cancer. Based on a few features, machine learning techniques can help in the diagnosis of lung cancer. The performance of several classifiers: Support Vector Machine (SVM), Logistic Regression (LR), Naïve Bayes (NB), Random Forest (RF), and K Nearest Neighbor (KNN), was evaluated by the authors using the dataset available on Kaggle to create a systematic approach for the diagnosis of lung cancer disease based on readily observable signs and historical medical data without the requirement of CT scan images. The authors have proposed a novel approach for classification called PCWKNN, which is a modified version of KNN and uses Pearson correlation coefficient values to determine weights in a weighted KNN. The performance of the classifiers was evaluated using the hold-out validation method. SVM, LR, and RF were 96.77% accurate. NB obtained 95.16% accuracy. KNN achieved 91.93% accuracy. PCWKNN outperformed the employed classifiers and obtained an accuracy of 98.39%.

Published Date: 2025-01-24; Received Date: 2023-10-11