Performance Comparison of Neural Network-Based Models in the Classification of Polycystic Ovary Syndrome Disease

Zeynep Küçükakçalı; Fatma Hilal Yağın; İpek Balıkçı Çiçek

Article Details

Performance Comparison of Neural Network-Based Models in the Classification of Polycystic Ovary Syndrome Disease

Authors:

Zeynep Küçükakçalı | Fatma Hilal Yağın | İpek Balıkçı Çiçek

Publisher:

Karyay Karadeniz Yayımcılık

Main Topics:

Klinik Tıp Bilimleri

Journal:

Black Sea Journal of Health Science Vol.6 Issue 1, pp.20-25

Document Type:

Article

Publication Date:

1/2023

Document Language:

English

Document ID (Org No.):

2001281468

pISSN:

2619-9041

Keywords:

Abstract:

 In this study, it was aimed to compare the performances of the above mentioned ANN, MLP and deep learning methods to determine polycystic ovary syndrome PCOS risk factors and predict PCOS diagnosis. In this study, the data set Polycystic ovary syndrome was used to determine PCOS risk factors and to compare the performances of ANN, MLP and deep learning methods for PCOS diagnosis prediction. The performance of the models was evaluated with accuracy, sensitivity, specificity, positivenegative predictive values. Factors associated with PCOS were estimated from the deep learning model that has the best performance. Accuracy, sensitivity, specificity, positive predictive value and negative predictive value obtained from the MLP method were 87.25, 79.66, 90.93, 81.03, and 90.19. Accuracy, sensitivity, specificity, positive predictive value and negative predictive value obtained from the Neural Network method were 87.80, 79.10, 92.03, 82.84, and 90.05. Accuracy, sensitivity, specificity, positive predictive value and negative predictive value obtained from the Deep Learning method were 89.09, 81.92, 92.58, 84.30, and 91.33. According to the findings obtained from this study, the best classification result according to the performance metrics obtained from the artificial neural networks, MLP and deep learning methods used for the PCOS data set used in the study belongs to the deep learning method. As a result, PCOS was successfully classified in the light of the findings obtained from the study, and clinical findings were tried to be revealed by giving the risk factors associated with PCOS.

 In this study, it was aimed to compare the performances of the above mentioned ANN, MLP and deep learning methods to determine polycystic ovary syndrome PCOS risk factors and predict PCOS diagnosis. In this study, the data set Polycystic ovary syndrome was used to determine PCOS risk factors and to compare the performances of ANN, MLP and deep learning methods for PCOS diagnosis prediction. The performance of the models was evaluated with accuracy, sensitivity, specificity, positivenegative predictive values. Factors associated with PCOS were estimated from the deep learning model that has the best performance. Accuracy, sensitivity, specificity, positive predictive value and negative predictive value obtained from the MLP method were 87.25, 79.66, 90.93, 81.03, and 90.19. Accuracy, sensitivity, specificity, positive predictive value and negative predictive value obtained from the Neural Network method were 87.80, 79.10, 92.03, 82.84, and 90.05. Accuracy, sensitivity, specificity, positive predictive value and negative predictive value obtained from the Deep Learning method were 89.09, 81.92, 92.58, 84.30, and 91.33. According to the findings obtained from this study, the best classification result according to the performance metrics obtained from the artificial neural networks, MLP and deep learning methods used for the PCOS data set used in the study belongs to the deep learning method. As a result, PCOS was successfully classified in the light of the findings obtained from the study, and clinical findings were tried to be revealed by giving the risk factors associated with PCOS.

 In this study, it was aimed to compare the performances of the above mentioned ANN, MLP and deep learning methods to determine polycystic ovary syndrome PCOS risk factors and predict PCOS diagnosis. In this study, the data set Polycystic ovary syndrome was used to determine PCOS risk factors and to compare the performances of ANN, MLP and deep learning methods for PCOS diagnosis prediction. The performance of the models was evaluated with accuracy, sensitivity, specificity, positivenegative predictive values. Factors associated with PCOS were estimated from the deep learning model that has the best performance. Accuracy, sensitivity, specificity, positive predictive value and negative predictive value obtained from the MLP method were 87.25, 79.66, 90.93, 81.03, and 90.19. Accuracy, sensitivity, specificity, positive predictive value and negative predictive value obtained from the Neural Network method were 87.80, 79.10, 92.03, 82.84, and 90.05. Accuracy, sensitivity, specificity, positive predictive value and negative predictive value obtained from the Deep Learning method were 89.09, 81.92, 92.58, 84.30, and 91.33. According to the findings obtained from this study, the best classification result according to the performance metrics obtained from the artificial neural networks, MLP and deep learning methods used for the PCOS data set used in the study belongs to the deep learning method. As a result, PCOS was successfully classified in the light of the findings obtained from the study, and clinical findings were tried to be revealed by giving the risk factors associated with PCOS.

 In this study, it was aimed to compare the performances of the above mentioned ANN, MLP and deep learning methods to determine polycystic ovary syndrome PCOS risk factors and predict PCOS diagnosis. In this study, the data set Polycystic ovary syndrome was used to determine PCOS risk factors and to compare the performances of ANN, MLP and deep learning methods for PCOS diagnosis prediction. The performance of the models was evaluated with accuracy, sensitivity, specificity, positivenegative predictive values. Factors associated with PCOS were estimated from the deep learning model that has the best performance. Accuracy, sensitivity, specificity, positive predictive value and negative predictive value obtained from the MLP method were 87.25, 79.66, 90.93, 81.03, and 90.19. Accuracy, sensitivity, specificity, positive predictive value and negative predictive value obtained from the Neural Network method were 87.80, 79.10, 92.03, 82.84, and 90.05. Accuracy, sensitivity, specificity, positive predictive value and negative predictive value obtained from the Deep Learning method were 89.09, 81.92, 92.58, 84.30, and 91.33. According to the findings obtained from this study, the best classification result according to the performance metrics obtained from the artificial neural networks, MLP and deep learning methods used for the PCOS data set used in the study belongs to the deep learning method. As a result, PCOS was successfully classified in the light of the findings obtained from the study, and clinical findings were tried to be revealed by giving the risk factors associated with PCOS.

Article Details

Performance Comparison of Neural Network-Based Models in the Classification of Polycystic Ovary Syndrome Disease

Authors:

Zeynep Küçükakçalı | Fatma Hilal Yağın | İpek Balıkçı Çiçek

Publisher:

Karyay Karadeniz Yayımcılık

Main Topics:

Klinik Tıp Bilimleri

Journal:

Black Sea Journal of Health Science Vol.6 Issue 1, pp.20-25

Document Type:

Article

Publication Date:

1/2023

Document Language:

English

Document ID (Org No.):

2001281468

pISSN:

2619-9041

PCOS

MLP

ANN

Deep learning

Classification

PCOS

MLP

ANN

Deep learning

Classification

Issues

Article Details

Authors: Zeynep Küçükakçalı | Fatma Hilal Yağın | İpek Balıkçı Çiçek

Publisher: Karyay Karadeniz Yayımcılık

Main Topics: Klinik Tıp Bilimleri

Journal: Black Sea Journal of Health Science Vol.6 Issue 1, pp.20-25

Document Type: Article

Publication Date: 1/2023

Document Language: English

Document ID (Org No.): 2001281468

pISSN: 2619-9041

Issues

Authors:

Zeynep Küçükakçalı | Fatma Hilal Yağın | İpek Balıkçı Çiçek

Publisher:

Karyay Karadeniz Yayımcılık

Main Topics:

Klinik Tıp Bilimleri

Journal:

Black Sea Journal of Health Science Vol.6 Issue 1, pp.20-25

Document Type:

Article

Publication Date:

1/2023

Document Language:

English

Document ID (Org No.):

2001281468

pISSN:

2619-9041