Evaluation of artificial neural network and adaptive-network-based fuzzy inference system for ovarian and lung cancer prediction

Semih Latif İpek; Dilek Göktürk

Makale detayları

Evaluation of artificial neural network and adaptive-network-based fuzzy inference system for ovarian and lung cancer prediction

Yazarlar:

Semih Latif İpek | Dilek Göktürk

Yayıncı:

MediHealth Academy Yayıncılık

Ana Başlıklar:

Karar Desteği ve Grup Destek Sistemleri

Dergi:

Journal of Health Sciences and Medicine Vol.7 Issue 1, pp.80-88

Belge Tipi:

Makale

Yayın Tarihi:

1/2024

Belge Dili:

İngilizce

Document ID (Org No.):

2001309610

pISSN:

2636-8579

Anahtar kelimeler:

Özet:

 Aims: Every year, a significant number of individuals lose their lives due to cancer or undergo challenging treatments. Indeed, the development of an effective cancer prediction method holds great importance in the field of healthcare. Methods: Machine learning methods have played a significant role in advancing cancer prediction models. In this context, this study focuses on exploring the potential of two machine learning methods: Artificial neural network ANN and adaptive-network-based fuzzy inference system ANFIS for cancer prediction. In this study, two different types of cancer, ovarian cancer and lung cancer, are taken into consideration. For the prediction of ovarian cancer, three specific biomarkers, namely human epididymis protein 4 HE4, carbohydrate antigen 125 CA-125, and carcinoembryonic antigen CEA, are used to develop a prediction model. For the prediction of lung cancer, six different variables are utilized in the development of both the ANN and ANFIS methods. Results: The findings demonstrated that the proposed methods had an accuracy rate of at least 93.9 in predicting ovarian cancer. With an accuracy rate of at least 89, the proposed methods predicted lung cancer. Also, the proposed ANN method outperforms the ANFIS method in terms of predictive accuracy for both ovarian cancer and lung cancer. Conclusion: This study suggests that the ANN method provides more reliable and accurate predictions for these specific cancer types based on the chosen variables or biomarkers. This study highlights the potential of machine learning methods, particularly ANN, in improving cancer prediction models and aiding in the early detection and effective management of ovarian and lung cancers.

 Aims: Every year, a significant number of individuals lose their lives due to cancer or undergo challenging treatments. Indeed, the development of an effective cancer prediction method holds great importance in the field of healthcare. Methods: Machine learning methods have played a significant role in advancing cancer prediction models. In this context, this study focuses on exploring the potential of two machine learning methods: Artificial neural network ANN and adaptive-network-based fuzzy inference system ANFIS for cancer prediction. In this study, two different types of cancer, ovarian cancer and lung cancer, are taken into consideration. For the prediction of ovarian cancer, three specific biomarkers, namely human epididymis protein 4 HE4, carbohydrate antigen 125 CA-125, and carcinoembryonic antigen CEA, are used to develop a prediction model. For the prediction of lung cancer, six different variables are utilized in the development of both the ANN and ANFIS methods. Results: The findings demonstrated that the proposed methods had an accuracy rate of at least 93.9 in predicting ovarian cancer. With an accuracy rate of at least 89, the proposed methods predicted lung cancer. Also, the proposed ANN method outperforms the ANFIS method in terms of predictive accuracy for both ovarian cancer and lung cancer. Conclusion: This study suggests that the ANN method provides more reliable and accurate predictions for these specific cancer types based on the chosen variables or biomarkers. This study highlights the potential of machine learning methods, particularly ANN, in improving cancer prediction models and aiding in the early detection and effective management of ovarian and lung cancers.

 Aims: Every year, a significant number of individuals lose their lives due to cancer or undergo challenging treatments. Indeed, the development of an effective cancer prediction method holds great importance in the field of healthcare. Methods: Machine learning methods have played a significant role in advancing cancer prediction models. In this context, this study focuses on exploring the potential of two machine learning methods: Artificial neural network ANN and adaptive-network-based fuzzy inference system ANFIS for cancer prediction. In this study, two different types of cancer, ovarian cancer and lung cancer, are taken into consideration. For the prediction of ovarian cancer, three specific biomarkers, namely human epididymis protein 4 HE4, carbohydrate antigen 125 CA-125, and carcinoembryonic antigen CEA, are used to develop a prediction model. For the prediction of lung cancer, six different variables are utilized in the development of both the ANN and ANFIS methods. Results: The findings demonstrated that the proposed methods had an accuracy rate of at least 93.9 in predicting ovarian cancer. With an accuracy rate of at least 89, the proposed methods predicted lung cancer. Also, the proposed ANN method outperforms the ANFIS method in terms of predictive accuracy for both ovarian cancer and lung cancer. Conclusion: This study suggests that the ANN method provides more reliable and accurate predictions for these specific cancer types based on the chosen variables or biomarkers. This study highlights the potential of machine learning methods, particularly ANN, in improving cancer prediction models and aiding in the early detection and effective management of ovarian and lung cancers.

 Aims: Every year, a significant number of individuals lose their lives due to cancer or undergo challenging treatments. Indeed, the development of an effective cancer prediction method holds great importance in the field of healthcare. Methods: Machine learning methods have played a significant role in advancing cancer prediction models. In this context, this study focuses on exploring the potential of two machine learning methods: Artificial neural network ANN and adaptive-network-based fuzzy inference system ANFIS for cancer prediction. In this study, two different types of cancer, ovarian cancer and lung cancer, are taken into consideration. For the prediction of ovarian cancer, three specific biomarkers, namely human epididymis protein 4 HE4, carbohydrate antigen 125 CA-125, and carcinoembryonic antigen CEA, are used to develop a prediction model. For the prediction of lung cancer, six different variables are utilized in the development of both the ANN and ANFIS methods. Results: The findings demonstrated that the proposed methods had an accuracy rate of at least 93.9 in predicting ovarian cancer. With an accuracy rate of at least 89, the proposed methods predicted lung cancer. Also, the proposed ANN method outperforms the ANFIS method in terms of predictive accuracy for both ovarian cancer and lung cancer. Conclusion: This study suggests that the ANN method provides more reliable and accurate predictions for these specific cancer types based on the chosen variables or biomarkers. This study highlights the potential of machine learning methods, particularly ANN, in improving cancer prediction models and aiding in the early detection and effective management of ovarian and lung cancers.

Makale detayları

Evaluation of artificial neural network and adaptive-network-based fuzzy inference system for ovarian and lung cancer prediction

Yazarlar: Semih Latif İpek | Dilek Göktürk

Yayıncı: MediHealth Academy Yayıncılık

Ana Başlıklar: Karar Desteği ve Grup Destek Sistemleri

Dergi: Journal of Health Sciences and Medicine Vol.7 Issue 1, pp.80-88

Belge Tipi: Makale

Yayın Tarihi: 1/2024

Belge Dili: İngilizce

Document ID (Org No.): 2001309610

pISSN: 2636-8579

ANN

ANFIS

cancer prediction

Sayı

Yazarlar:

Semih Latif İpek | Dilek Göktürk

Yayıncı:

MediHealth Academy Yayıncılık

Ana Başlıklar:

Karar Desteği ve Grup Destek Sistemleri

Dergi:

Journal of Health Sciences and Medicine Vol.7 Issue 1, pp.80-88

Belge Tipi:

Makale

Yayın Tarihi:

1/2024

Belge Dili:

İngilizce

Document ID (Org No.):

2001309610

pISSN:

2636-8579