Hybrid GA-SVM Approach for Postoperative Life Expectancy Prediction in Lung Cancer Patients
Medical outcomes must be tracked in order to enhance quality initiatives, healthcare management, and mass education. Thoracic surgery data have been acquired for those who underwent major lung surgery for primary lung cancer, a field in which there has been little research and few reliable recommend...
| Main Authors: | , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2022-10-01
|
| Series: | Applied Sciences |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2076-3417/12/21/10927 |
| _version_ | 1797469225215852544 |
|---|---|
| author | Arfan Ali Nagra Iqra Mubarik Muhammad Mugees Asif Khalid Masood Mohammed A. Al Ghamdi Sultan H. Almotiri |
| author_facet | Arfan Ali Nagra Iqra Mubarik Muhammad Mugees Asif Khalid Masood Mohammed A. Al Ghamdi Sultan H. Almotiri |
| author_sort | Arfan Ali Nagra |
| collection | DOAJ |
| description | Medical outcomes must be tracked in order to enhance quality initiatives, healthcare management, and mass education. Thoracic surgery data have been acquired for those who underwent major lung surgery for primary lung cancer, a field in which there has been little research and few reliable recommendations have been made for lung cancer patients. Early detection of lung cancer benefits therapy choices and increases the odds of a patient surviving a lung cancer infection. Using a Hybrid Genetic and Support Vector Machine (GA-SVM) methodology, this study proposes a method for identifying lung cancer patients. To estimate postoperative life expectancy, ensemble machine-learning techniques were applied. The article also presents a strategy for estimating a patient’s life expectancy following thoracic surgery after the detection of cancer. To perform the prediction, hybrid machine-learning methods were applied. In ensemble machine-learning algorithms, attribute ranking and selection are critical components of robust health outcome prediction. To enhance the efficacy of algorithms in health data analysis, we propose three attribute ranking and selection procedures. Compared to other machine-learning techniques, GA-SVM achieves an accuracy of 85% and a higher F1 score of 0.92. The proposed algorithm was compared with two recent state-of-the-art techniques and its performance level was ranked superior to those of its counterparts. |
| first_indexed | 2024-03-09T19:18:21Z |
| format | Article |
| id | doaj.art-a3cf9872717a4c879c81544fe1d1a9b6 |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-09T19:18:21Z |
| publishDate | 2022-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-a3cf9872717a4c879c81544fe1d1a9b62023-11-24T03:35:11ZengMDPI AGApplied Sciences2076-34172022-10-0112211092710.3390/app122110927Hybrid GA-SVM Approach for Postoperative Life Expectancy Prediction in Lung Cancer PatientsArfan Ali Nagra0Iqra Mubarik1Muhammad Mugees Asif2Khalid Masood3Mohammed A. Al Ghamdi4Sultan H. Almotiri5Department of Computer Science, Lahore Garrison University, Lahore 94777, PakistanDepartment of Computer Science, Lahore Garrison University, Lahore 94777, PakistanDepartment of Computer Science, Lahore Garrison University, Lahore 94777, PakistanDepartment of Computer Science, Lahore Garrison University, Lahore 94777, PakistanComputer Science Department, Umm Al-Qura University, Mecca 21961, Saudi ArabiaComputer Science Department, Umm Al-Qura University, Mecca 21961, Saudi ArabiaMedical outcomes must be tracked in order to enhance quality initiatives, healthcare management, and mass education. Thoracic surgery data have been acquired for those who underwent major lung surgery for primary lung cancer, a field in which there has been little research and few reliable recommendations have been made for lung cancer patients. Early detection of lung cancer benefits therapy choices and increases the odds of a patient surviving a lung cancer infection. Using a Hybrid Genetic and Support Vector Machine (GA-SVM) methodology, this study proposes a method for identifying lung cancer patients. To estimate postoperative life expectancy, ensemble machine-learning techniques were applied. The article also presents a strategy for estimating a patient’s life expectancy following thoracic surgery after the detection of cancer. To perform the prediction, hybrid machine-learning methods were applied. In ensemble machine-learning algorithms, attribute ranking and selection are critical components of robust health outcome prediction. To enhance the efficacy of algorithms in health data analysis, we propose three attribute ranking and selection procedures. Compared to other machine-learning techniques, GA-SVM achieves an accuracy of 85% and a higher F1 score of 0.92. The proposed algorithm was compared with two recent state-of-the-art techniques and its performance level was ranked superior to those of its counterparts.https://www.mdpi.com/2076-3417/12/21/10927thoracic surgerydata wranglinggenetic algorithmsupport vector machinesurvival |
| spellingShingle | Arfan Ali Nagra Iqra Mubarik Muhammad Mugees Asif Khalid Masood Mohammed A. Al Ghamdi Sultan H. Almotiri Hybrid GA-SVM Approach for Postoperative Life Expectancy Prediction in Lung Cancer Patients Applied Sciences thoracic surgery data wrangling genetic algorithm support vector machine survival |
| title | Hybrid GA-SVM Approach for Postoperative Life Expectancy Prediction in Lung Cancer Patients |
| title_full | Hybrid GA-SVM Approach for Postoperative Life Expectancy Prediction in Lung Cancer Patients |
| title_fullStr | Hybrid GA-SVM Approach for Postoperative Life Expectancy Prediction in Lung Cancer Patients |
| title_full_unstemmed | Hybrid GA-SVM Approach for Postoperative Life Expectancy Prediction in Lung Cancer Patients |
| title_short | Hybrid GA-SVM Approach for Postoperative Life Expectancy Prediction in Lung Cancer Patients |
| title_sort | hybrid ga svm approach for postoperative life expectancy prediction in lung cancer patients |
| topic | thoracic surgery data wrangling genetic algorithm support vector machine survival |
| url | https://www.mdpi.com/2076-3417/12/21/10927 |
| work_keys_str_mv | AT arfanalinagra hybridgasvmapproachforpostoperativelifeexpectancypredictioninlungcancerpatients AT iqramubarik hybridgasvmapproachforpostoperativelifeexpectancypredictioninlungcancerpatients AT muhammadmugeesasif hybridgasvmapproachforpostoperativelifeexpectancypredictioninlungcancerpatients AT khalidmasood hybridgasvmapproachforpostoperativelifeexpectancypredictioninlungcancerpatients AT mohammedaalghamdi hybridgasvmapproachforpostoperativelifeexpectancypredictioninlungcancerpatients AT sultanhalmotiri hybridgasvmapproachforpostoperativelifeexpectancypredictioninlungcancerpatients |