Detection of Coronary Heart Disease by Data Mining Methods Using Clinical Data

Year 2022, Volume: 9 Issue: 3, 104 - 109, 31.12.2022

Burak Yağın

Abstract

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.

Keywords

Heart disease, artificial intelligence, data mining, random forest, parameter optimization

References

• 1. Bruni L, Albero G, Serrano B, Mena M, Gomez D, Munoz J, et al. Information Centre on HPV and Cancer (HPV Information Centre). Human papillomavirus and related diseases in the world. Summary Report. 2019.
• 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.
• 8. Haykin S. Neural Networks, a comprehensive foundation, Prentice-Hall Inc. Upper Saddle River, New Jersey. 1999;7458:161-75.
• 9. Batar H. Classification of EEG signals with artificial neural networks using wavelet analysis methods. Kahramanmaras: Institute of science, Master Thesis. 2005.
• 10. Arı A, Berberler ME. Interface Design for the Solution of Prediction and Classification Problems with Artificial Neural Networks. 2017;1(2):55-73.
• 11. Golafshani EM, Behnood A. Application of soft computing methods for predicting the elastic modulus of recycled aggregate concrete. Journal of cleaner production. 2018;176:1163-76.
• 12. Orhan U, Hekim M, Ozer M, et al. Discretization approach to EEG signal classification using Multilayer Perceptron Neural Network model. 2010 15th National Biomedical Engineering Meeting; 2010: IEEE.
• 13. Kaynar O, Gormez Y, Işık YE, Demirkoparan F, et al. Intrusion Detection with Radial Based Artificial Neural Networks Trained with Different Clustering Algorithms. International Artificial Intelligence and Data Processing Symposium; 2016.
• 14. Saha A, Chaudhury AN, Bhowmik P, Chatterjee R. Awareness of cervical cancer among female students of premier colleges in Kolkata, India. Asian Pac J Cancer Prev. 2010;11(4):1085-90.
• 15. Priya NS. Cervical cancer screening and classification using acoustic shadowing. International Journal of innovatibe Research in Computer and Communication Engineering. 2013;1(8):1676-9.
• 16. Wu M, Yan C, Liu H, Liu Q. Automatic classification of ovarian cancer types from cytological images using deep convolutional neural networks. Bioscience reports. 2018;38(3).
• 17. Akter L, Islam M, Al-Rakhami MS, Haque M. Prediction of cervical cancer from behavior risk using machine learning techniques. SN Computer Science. 2021;2(3):1-10.
• 18. Tseng CJ, Lu CJ, Chang CC, Chen GD. Application of machine learning to predict the recurrence-proneness for cervical cancer. Neural Computing and Applications. 2014;24(6):1311-6.
• 19. Machmud R, Wijaya A. Behavior determinant based cervical cancer early detection with machine learning algorithm. Advanced Science Letters. 2016;22(10):3120-3.
• 20. Sharma S. Cervical cancer stage prediction using decision tree approach of machine learning. International Journal of Advanced Research in Computer and Communication Engineering. 2016;5(4):345-8.
• 21. Lu J, Song E, Ghoneim A, Alrashoud M. Machine learning for assisting cervical cancer diagnosis: An ensemble approach. Future Generation Computer Systems. 2020;106:199-205.
• 22. Wu W, Zhou H. Data-driven diagnosis of cervical cancer with support vector machine-based approaches. IEEE Access. 2017;5:25189-95.
• 23. Hyeon J, Choi HJ, Lee KN, Lee BD, et al. Automating papanicolaou test using deep convolutional activation feature. 18th IEEE international conference on Mobile Data Management (MDM); 2017.
• 24. Nasser IM, Abu-Naser SS. Lung Cancer Detection Using Artificial Neural Network. J. Eng. Technol. 2019;3(3):17-23.