Prediction of Lung Cancer Disease Using Machine Learning Techniques
The pursuit of algorithms utilizing external examples to formulate extensive hypotheses predicting the occurrence of novel instances is recognized, as supervised machine learning (SML). One of the jobs that intelligent systems perform the most frequently is supervised classification. The goal of thi...
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| Language: | English |
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University of Human Development
2024-11-01
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| Series: | UHD Journal of Science and Technology |
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| Online Access: | https://journals.uhd.edu.iq/index.php/uhdjst/article/view/1318 |
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| author | Rukhsar Hatam Qadir Karwan Mohammed HamaKarim |
| author_facet | Rukhsar Hatam Qadir Karwan Mohammed HamaKarim |
| author_sort | Rukhsar Hatam Qadir |
| collection | DOAJ |
| description | The pursuit of algorithms utilizing external examples to formulate extensive hypotheses predicting the occurrence of novel instances is recognized, as supervised machine learning (SML). One of the jobs that intelligent systems perform the most frequently is supervised classification. The goal of this work is to evaluate supervised learning algorithms, explain SML classification methodologies, and identify the most effective classification algorithm given the available data. Two distinct machine learning (ML) techniques were examined: Random Forest (RF) and Neural Networks (NN). The algorithms were implemented using Python for knowledge analysis. For the categorization, 310 cases from a lung cancer data set were employed, with 15 features serving as independent variables and one serving as the dependent variable. In comparison to NN classification methods, RF was found to be the algorithm with the highest precision and accuracy, according to the results. The study reveals that while the kappa statistic and mean square error (MSE) are factors on the one hand, the time required to create a model and precision (accuracy) are factors on the other. Consequently, to have supervised predictive ML algorithms need to be precise, accurate, and minimum error. Thus, as a consequence of the research, we are currently at this analysis. The categorizing of NNs accuracy is 0.75 the MSE is 0.25, The RF classification accuracy is 0.89 and the MSE is 0.21. |
| format | Article |
| id | doaj-art-92b0f9edf063481ea10c7b64c0ebf281 |
| institution | Kabale University |
| issn | 2521-4209 2521-4217 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | University of Human Development |
| record_format | Article |
| series | UHD Journal of Science and Technology |
| spelling | doaj-art-92b0f9edf063481ea10c7b64c0ebf2812024-12-07T11:04:13ZengUniversity of Human DevelopmentUHD Journal of Science and Technology2521-42092521-42172024-11-0182758310.21928/uhdjst.v8n2y2024.pp75-831451Prediction of Lung Cancer Disease Using Machine Learning TechniquesRukhsar Hatam Qadir0Karwan Mohammed HamaKarim1Department of Statistics and Informatics, College-of-administration-and-economics, University of Sulaimani, Sulaimani, Kurdistan Region-IraqDepartment of Information Technology, College of science and technology, University of Human Development, Sulaimani, Kurdistan Region-IraqThe pursuit of algorithms utilizing external examples to formulate extensive hypotheses predicting the occurrence of novel instances is recognized, as supervised machine learning (SML). One of the jobs that intelligent systems perform the most frequently is supervised classification. The goal of this work is to evaluate supervised learning algorithms, explain SML classification methodologies, and identify the most effective classification algorithm given the available data. Two distinct machine learning (ML) techniques were examined: Random Forest (RF) and Neural Networks (NN). The algorithms were implemented using Python for knowledge analysis. For the categorization, 310 cases from a lung cancer data set were employed, with 15 features serving as independent variables and one serving as the dependent variable. In comparison to NN classification methods, RF was found to be the algorithm with the highest precision and accuracy, according to the results. The study reveals that while the kappa statistic and mean square error (MSE) are factors on the one hand, the time required to create a model and precision (accuracy) are factors on the other. Consequently, to have supervised predictive ML algorithms need to be precise, accurate, and minimum error. Thus, as a consequence of the research, we are currently at this analysis. The categorizing of NNs accuracy is 0.75 the MSE is 0.25, The RF classification accuracy is 0.89 and the MSE is 0.21.https://journals.uhd.edu.iq/index.php/uhdjst/article/view/1318machine learningclassifiersdata mining techniquesdata analysislearning algorithmssupervised machine learning |
| spellingShingle | Rukhsar Hatam Qadir Karwan Mohammed HamaKarim Prediction of Lung Cancer Disease Using Machine Learning Techniques UHD Journal of Science and Technology machine learning classifiers data mining techniques data analysis learning algorithms supervised machine learning |
| title | Prediction of Lung Cancer Disease Using Machine Learning Techniques |
| title_full | Prediction of Lung Cancer Disease Using Machine Learning Techniques |
| title_fullStr | Prediction of Lung Cancer Disease Using Machine Learning Techniques |
| title_full_unstemmed | Prediction of Lung Cancer Disease Using Machine Learning Techniques |
| title_short | Prediction of Lung Cancer Disease Using Machine Learning Techniques |
| title_sort | prediction of lung cancer disease using machine learning techniques |
| topic | machine learning classifiers data mining techniques data analysis learning algorithms supervised machine learning |
| url | https://journals.uhd.edu.iq/index.php/uhdjst/article/view/1318 |
| work_keys_str_mv | AT rukhsarhatamqadir predictionoflungcancerdiseaseusingmachinelearningtechniques AT karwanmohammedhamakarim predictionoflungcancerdiseaseusingmachinelearningtechniques |