EN
Application with Multilayer Perceptron and Radial Basis Function from Neural Network-Based Methods to Predict Cervical Cancer
Öz
Objective: Cervical cancer is the fourth most prevalent malignancy among women worldwide. Low- and middle-income countries are much more burdened than high-income nations. Therefore, the need to develop new diagnostic techniques to predict the course of the disease and the prognosis of this malignancy has increased. In this study, cervical cancer will be classified to create an accurate diagnostic predictive model using the machine learning method The Multilayer Perceptron (MLPNN) and Radial Based ANN (RBFNN), and disease-related risk factors will be determined.
Methods: This current study considered the open-access data set of patients that cervical cancer and no-cervical cancer samples. For this purpose, data from 72 patients were included. The data set was divided as 80:20 as a training and test dataset. MLPNN and RBFNN were used for the classification Accuracy, specificity, AUC, positive predictive value, and negative predictive value performance metrics were evaluated for model performance.
Results: Among the performance criteria in the test stage obtained from the RBFNN model that has the best classification result; accuracy, specificity, AUC, positive predictive value, and negative predictive value were obtained as 92.3%, 100.0%, 96.5%, 100.0%, and 91.6%, respectively. According to the variable importance obtained as a result of the model, the variables most associated with the diagnosis were behavior sexual risk, empowerment abilities, and motivation strength, respectively.
Conclusion: The applied machine learning model successfully classified cervical cancer and created a highly accurate diagnostic prediction model. With the parameters determined as a result of the modeling, the clinician will be able to simplify and facilitate the decision-making process for the diagnosis of cervical cancer.
Methods: This current study considered the open-access data set of patients that cervical cancer and no-cervical cancer samples. For this purpose, data from 72 patients were included. The data set was divided as 80:20 as a training and test dataset. MLPNN and RBFNN were used for the classification Accuracy, specificity, AUC, positive predictive value, and negative predictive value performance metrics were evaluated for model performance.
Results: Among the performance criteria in the test stage obtained from the RBFNN model that has the best classification result; accuracy, specificity, AUC, positive predictive value, and negative predictive value were obtained as 92.3%, 100.0%, 96.5%, 100.0%, and 91.6%, respectively. According to the variable importance obtained as a result of the model, the variables most associated with the diagnosis were behavior sexual risk, empowerment abilities, and motivation strength, respectively.
Conclusion: The applied machine learning model successfully classified cervical cancer and created a highly accurate diagnostic prediction model. With the parameters determined as a result of the modeling, the clinician will be able to simplify and facilitate the decision-making process for the diagnosis of cervical cancer.
Anahtar Kelimeler
Kaynakça
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- 2. Kombe Kombe AJ, Li B, Zahid A, Mengist HM, Bounda G-A, Zhou Y, et al. Epidemiology and burden of human papillomavirus and related diseases, molecular pathogenesis, and vaccine evaluation. Frontiers in public health. 2021;8:552028.
- 3. Eyupoğlu C. A New Cervical Cancer Diagnosis Method Based on Correlation-Based Trait Selection, Genetic Search, and Random Forests Techniques. European Journal of Science and Technology.2020;19:263-71.
- 4. Singh HD. Diagnosis of Cervical Cancer using Hybrid Machine Learning Models: Dublin, National College of Ireland; 2018.
- 5. Sokouti B, Haghipour S, Tabrizi AD. A framework for diagnosing cervical cancer disease based on feedforward MLP neural network and ThinPrep histopathological cell image features. Neural Computing and Applications. 2014;24(1):221-32.
- 6. Oztemel E. Artificial neural networks, Daisy Publishing. First Edition. Istanbul;2003.p.48.
- 7. Siddique R, Aggarwal P, Aggarwal Y. Prediction of compressive strength of self-compacting concrete containing bottom ash using artificial neural networks. Advances in engineering software. 2011;42(10):780-6.
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Ayrıntılar
Birincil Dil
İngilizce
Konular
Sağlık Kurumları Yönetimi
Bölüm
Araştırma Makalesi
Yazarlar
Yayımlanma Tarihi
31 Aralık 2022
Gönderilme Tarihi
22 Eylül 2022
Kabul Tarihi
17 Kasım 2022
Yayımlandığı Sayı
Yıl 2022 Cilt: 9 Sayı: 3
APA
Balıkçı Çiçek, İ., & Küçükakçalı, Z. (2022). Application with Multilayer Perceptron and Radial Basis Function from Neural Network-Based Methods to Predict Cervical Cancer. ODÜ Tıp Dergisi, 9(3), 84-93. https://izlik.org/JA55RY32NU
AMA
1.Balıkçı Çiçek İ, Küçükakçalı Z. Application with Multilayer Perceptron and Radial Basis Function from Neural Network-Based Methods to Predict Cervical Cancer. ODU Tıp Derg. 2022;9(3):84-93. https://izlik.org/JA55RY32NU
Chicago
Balıkçı Çiçek, İpek, ve Zeynep Küçükakçalı. 2022. “Application with Multilayer Perceptron and Radial Basis Function from Neural Network-Based Methods to Predict Cervical Cancer”. ODÜ Tıp Dergisi 9 (3): 84-93. https://izlik.org/JA55RY32NU.
EndNote
Balıkçı Çiçek İ, Küçükakçalı Z (01 Aralık 2022) Application with Multilayer Perceptron and Radial Basis Function from Neural Network-Based Methods to Predict Cervical Cancer. ODÜ Tıp Dergisi 9 3 84–93.
IEEE
[1]İ. Balıkçı Çiçek ve Z. Küçükakçalı, “Application with Multilayer Perceptron and Radial Basis Function from Neural Network-Based Methods to Predict Cervical Cancer”, ODU Tıp Derg, c. 9, sy 3, ss. 84–93, Ara. 2022, [çevrimiçi]. Erişim adresi: https://izlik.org/JA55RY32NU
ISNAD
Balıkçı Çiçek, İpek - Küçükakçalı, Zeynep. “Application with Multilayer Perceptron and Radial Basis Function from Neural Network-Based Methods to Predict Cervical Cancer”. ODÜ Tıp Dergisi 9/3 (01 Aralık 2022): 84-93. https://izlik.org/JA55RY32NU.
JAMA
1.Balıkçı Çiçek İ, Küçükakçalı Z. Application with Multilayer Perceptron and Radial Basis Function from Neural Network-Based Methods to Predict Cervical Cancer. ODU Tıp Derg. 2022;9:84–93.
MLA
Balıkçı Çiçek, İpek, ve Zeynep Küçükakçalı. “Application with Multilayer Perceptron and Radial Basis Function from Neural Network-Based Methods to Predict Cervical Cancer”. ODÜ Tıp Dergisi, c. 9, sy 3, Aralık 2022, ss. 84-93, https://izlik.org/JA55RY32NU.
Vancouver
1.İpek Balıkçı Çiçek, Zeynep Küçükakçalı. Application with Multilayer Perceptron and Radial Basis Function from Neural Network-Based Methods to Predict Cervical Cancer. ODU Tıp Derg [Internet]. 01 Aralık 2022;9(3):84-93. Erişim adresi: https://izlik.org/JA55RY32NU