Your browser doesn't support javascript.
loading
Optimizing lung cancer classification through hyperparameter tuning.
Nabeel, Syed Muhammad; Bazai, Sibghat Ullah; Alasbali, Nada; Liu, Yifan; Ghafoor, Muhammad Imran; Khan, Rozi; Ku, Chin Soon; Yang, Jing; Shahab, Sana; Por, Lip Yee.
Afiliación
  • Nabeel SM; Department of Computer Engineering, Balochistan University of Information Technology, Engineering, and Management Sciences (BUITEMS), Quetta, Balochistan, Pakistan.
  • Bazai SU; Department of Computer Engineering, Balochistan University of Information Technology, Engineering, and Management Sciences (BUITEMS), Quetta, Balochistan, Pakistan.
  • Alasbali N; Department of Informatics and Computing Systems, College of Computer Science, King Khalid University, Abha, Saudi Arabia.
  • Liu Y; Department of Electronic Science, Binhai College of Nankai University, Tianjing, China.
  • Ghafoor MI; Department of Engineering, Pakistan Television Corporation, Lahore, Pakistan.
  • Khan R; Department of Computer Science, National University of Sciences and Technology (NUST) Balochistan Campus Quetta, Quetta, Balochistan, Pakistan.
  • Ku CS; Department of Computer Science, Universiti Tunku Abdul Rahman, Kampar, Malaysia.
  • Yang J; Department of Computer System and Technology, Faculty of Computer Science and Information Technology, Universiti Malaya, Kuala Lumpur, Malaysia.
  • Shahab S; Department of Business Administration, College of Business Administration, Princess Nourah Bint Abdulrahman University, Riyadh, Saudi Arabia.
  • Por LY; Department of Computer System and Technology, Faculty of Computer Science and Information Technology, Universiti Malaya, Kuala Lumpur, Malaysia.
Digit Health ; 10: 20552076241249661, 2024.
Article en En | MEDLINE | ID: mdl-38698834
ABSTRACT
Artificial intelligence is steadily permeating various sectors, including healthcare. This research specifically addresses lung cancer, the world's deadliest disease with the highest mortality rate. Two primary factors contribute to its onset genetic predisposition and environmental factors, such as smoking and exposure to pollutants. Recognizing the need for more effective diagnosis techniques, our study embarked on devising a machine learning strategy tailored to boost precision in lung cancer detection. Our aim was to devise a diagnostic method that is both less invasive and cost-effective. To this end, we proposed four methods, benchmarking them against prevalent techniques using a universally recognized dataset from Kaggle. Among our methods, one emerged as particularly promising, outperforming the competition in accuracy, precision and sensitivity. This method utilized hyperparameter tuning, focusing on the Gamma and C parameters, which were set at a value of 10. These parameters influence kernel width and regularization strength, respectively. As a result, we achieved an accuracy of 99.16%, a precision of 98% and a sensitivity rate of 100%. In conclusion, our enhanced prediction mechanism has proven to surpass traditional and contemporary strategies in lung cancer detection.
Palabras clave
Texto completo
Añadir a Mi BVS
Imprimir
XML
PubMed Links
Buscar en Google

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Idioma: En Revista: Digit Health Año: 2024 Tipo del documento: Article País de afiliación: Pakistán Pais de publicación: Estados Unidos
Texto completo
Añadir a Mi BVS
Imprimir
XML
PubMed Links
Buscar en Google

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Idioma: En Revista: Digit Health Año: 2024 Tipo del documento: Article País de afiliación: Pakistán Pais de publicación: Estados Unidos