Machine learning-based lung cancer diagnosis

Mahmut Dirik

Makale detayları

Machine learning-based lung cancer diagnosis

Yazarlar:

Mahmut Dirik

Yayıncı:

Murat YAKAR

Ana Başlıklar:

Engineering

Dergi:

Turkish Journal of Engineering Vol.7 Issue 4, pp.322-330

Belge Tipi:

Makale

Yayın Tarihi:

5/2023

Belge Dili:

İngilizce

Document ID (Org No.):

2001284482

pISSN:

2587-1366

Anahtar kelimeler:

Özet:

 Cancer is one of the leading health problems, occurring in various organs and tissues of the body, and its incidence is increasing worldwide. Lung cancer is one of the deadliest types of cancer. Due to its worldwide prevalence, increasing number of cases, and deadly consequences, early detection of lung cancer, as with all other cancers, greatly increases the chances of survival. As with all other diseases, the diagnosis of cancer is only possible after the appearance of various symptoms and an examination by specialists. Known symptoms of lung cancer are shortness of breath, coughing, wheezing, jaundice in the fingers, chest pain, and difficulty swallowing. The diagnosis is made by an expert on site based on these symptoms and additional tests. The aim of this study is to detect the disease at an earlier stage based on the symptoms present, to assess more cases with less time and cost, and to achieve results in new situations that are as successful or even faster than those of human experts by deriving them from existing data using different algorithms. The aim is to develop an automated model that can detect early-stage lung cancer based on machine learning methods. The developed model includes nine different machine learning algorithms NB, LR, DT, RF, GB, and SVM. The success of the classification algorithms used was evaluated using the metrics of accuracy, sensitivity, and precision calculated using the parameters of the confusion matrix. The results obtained show that the proposed model can detect cancer with a maximum accuracy of 91.

 Cancer is one of the leading health problems, occurring in various organs and tissues of the body, and its incidence is increasing worldwide. Lung cancer is one of the deadliest types of cancer. Due to its worldwide prevalence, increasing number of cases, and deadly consequences, early detection of lung cancer, as with all other cancers, greatly increases the chances of survival. As with all other diseases, the diagnosis of cancer is only possible after the appearance of various symptoms and an examination by specialists. Known symptoms of lung cancer are shortness of breath, coughing, wheezing, jaundice in the fingers, chest pain, and difficulty swallowing. The diagnosis is made by an expert on site based on these symptoms and additional tests. The aim of this study is to detect the disease at an earlier stage based on the symptoms present, to assess more cases with less time and cost, and to achieve results in new situations that are as successful or even faster than those of human experts by deriving them from existing data using different algorithms. The aim is to develop an automated model that can detect early-stage lung cancer based on machine learning methods. The developed model includes nine different machine learning algorithms NB, LR, DT, RF, GB, and SVM. The success of the classification algorithms used was evaluated using the metrics of accuracy, sensitivity, and precision calculated using the parameters of the confusion matrix. The results obtained show that the proposed model can detect cancer with a maximum accuracy of 91.

 Cancer is one of the leading health problems, occurring in various organs and tissues of the body, and its incidence is increasing worldwide. Lung cancer is one of the deadliest types of cancer. Due to its worldwide prevalence, increasing number of cases, and deadly consequences, early detection of lung cancer, as with all other cancers, greatly increases the chances of survival. As with all other diseases, the diagnosis of cancer is only possible after the appearance of various symptoms and an examination by specialists. Known symptoms of lung cancer are shortness of breath, coughing, wheezing, jaundice in the fingers, chest pain, and difficulty swallowing. The diagnosis is made by an expert on site based on these symptoms and additional tests. The aim of this study is to detect the disease at an earlier stage based on the symptoms present, to assess more cases with less time and cost, and to achieve results in new situations that are as successful or even faster than those of human experts by deriving them from existing data using different algorithms. The aim is to develop an automated model that can detect early-stage lung cancer based on machine learning methods. The developed model includes nine different machine learning algorithms NB, LR, DT, RF, GB, and SVM. The success of the classification algorithms used was evaluated using the metrics of accuracy, sensitivity, and precision calculated using the parameters of the confusion matrix. The results obtained show that the proposed model can detect cancer with a maximum accuracy of 91.

 Cancer is one of the leading health problems, occurring in various organs and tissues of the body, and its incidence is increasing worldwide. Lung cancer is one of the deadliest types of cancer. Due to its worldwide prevalence, increasing number of cases, and deadly consequences, early detection of lung cancer, as with all other cancers, greatly increases the chances of survival. As with all other diseases, the diagnosis of cancer is only possible after the appearance of various symptoms and an examination by specialists. Known symptoms of lung cancer are shortness of breath, coughing, wheezing, jaundice in the fingers, chest pain, and difficulty swallowing. The diagnosis is made by an expert on site based on these symptoms and additional tests. The aim of this study is to detect the disease at an earlier stage based on the symptoms present, to assess more cases with less time and cost, and to achieve results in new situations that are as successful or even faster than those of human experts by deriving them from existing data using different algorithms. The aim is to develop an automated model that can detect early-stage lung cancer based on machine learning methods. The developed model includes nine different machine learning algorithms NB, LR, DT, RF, GB, and SVM. The success of the classification algorithms used was evaluated using the metrics of accuracy, sensitivity, and precision calculated using the parameters of the confusion matrix. The results obtained show that the proposed model can detect cancer with a maximum accuracy of 91.

Makale detayları

Machine learning-based lung cancer diagnosis

Yazarlar:

Mahmut Dirik

Yayıncı:

Murat YAKAR

Ana Başlıklar:

Engineering

Dergi:

Turkish Journal of Engineering Vol.7 Issue 4, pp.322-330

Belge Tipi:

Makale

Yayın Tarihi:

5/2023

Belge Dili:

İngilizce

Document ID (Org No.):

2001284482

pISSN:

2587-1366

Lung cancer

Early diagnosis

Machine learning

Classification

Sayı

Makale detayları

Machine learning-based lung cancer diagnosis

Yazarlar: Mahmut Dirik

Yayıncı: Murat YAKAR

Ana Başlıklar: Engineering

Dergi: Turkish Journal of Engineering Vol.7 Issue 4, pp.322-330

Belge Tipi: Makale

Yayın Tarihi: 5/2023

Belge Dili: İngilizce

Document ID (Org No.): 2001284482

pISSN: 2587-1366

Lung cancer

Early diagnosis

Machine learning

Classification

Sayı

Yazarlar:

Mahmut Dirik

Yayıncı:

Murat YAKAR

Ana Başlıklar:

Engineering

Dergi:

Turkish Journal of Engineering Vol.7 Issue 4, pp.322-330

Belge Tipi:

Makale

Yayın Tarihi:

5/2023

Belge Dili:

İngilizce

Document ID (Org No.):

2001284482

pISSN:

2587-1366