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Artificial Neural Network-Based Pelvic Inflammatory Disease Diagnosis System

Yahaya Mohammed Sani *, Dere Boluwatife Adesola**, Hussaini Abubakar Zubairu***, Ilyasu Anda****

*-*** Department of Information and Media Technology, Federal University of Technology, Minna, Nigeria.

**** Department of Library and Information Technology, Federal University of Technology, Minna, Nigeria.

Periodicity:March - May'2019
DOI : https://doi.org/10.26634/jpr.6.1.16510

Abstract

Pelvic Inflammatory Disease (PID) is a reproductive health infective disease of feminine genital tract and is commonly affecting the young women and adult female. Clinical manifestation of PID differs among patients and decision of medical experts are based on clinician experience instead of hidden data in the knowledge database. The diagnosis of PID based on heuristic lead to errors, where ectopic pregnancy could be mistaken for PID. This paper presents Artificial Neural Network based model to diagnose pelvic inflammatory diseases based on a set of clinical data. The ANN model was trained with 259 clinical data as input to the neural network. The system can predict the presence or absence of PID based on the available symptoms. An accuracy of 96.1% was recorded based on the confusion matrix. The obtained result is promising, an indication that the system can be effective in diagnosis of PID cases.

Keywords

Pelvic inflammatory disease; artificial neural network; computer simulation; diagnosis system; confusion matrix

How to Cite this Article?

Sani , Y., M., Adesola, D., B., Zubairu, H., A., & Anda, I. (2019). Artificial Neural Network-Based Pelvic Inflammatory Disease Diagnosis System. i-manager’s Journal on Pattern Recognition, 6(1), 1-10. https://doi.org/10.26634/jpr.6.1.16510

References

[1]. Ahmad, R. W. (2009). Pelvic Inflammatory Disease -An Ovarian Infection. Daily Trust. Retrieved from www.daily trust.com

[2]. Amato, F., López, J., Méndez, V. P., Hampl, & Havel, J. (2013). Artificial Neural Networks combined with experimental design: A soft approach for chemical kinetics. Talanta 93, 72-78. Retrieved from http://jab.zsf.jcu.cz// 11_2/havel.html

[3]. B.K.S. Gul, Z.L. Albati, R.R. I Badr, Z.M. Alfaraj, A.S. Almatrafi, A.A. Banoun, R.M.H. Maki, S.F. Bagdood, A.S. Aljuhani and A.S. Alahmari (2018), Pelvic Inflammatory Disease, Egyptian, Journal of Hospital Medicine, Vol 70 issue 9, pp. 1464-1467.

[4]. Bagdood, S., F., AlJuhani, A., S., & Alahmari, A., S. (2018). Pelvic Inflammatory Disease. The Egyptian Journal of Hospital Medicine, 70(9), 1464-1467.

[5]. Barbosa, D. C., Roupar, D. B., Ramos, J. C., Tavares, A. C., & Lima, C. S. (2012). Automatic small bowel tumor diagnosis by using multi-scale wavelet-based analysis in wireless capsule endoscopy images. Biomedical Engineering Online, 11(1), 3-17.

[6]. Barwad, A., Dey, P., & Susheilia, S. (2012). Artificial neural network in diagnosis of metastatic carcinoma in effusion cytology. Cytometry Part B: Clinical Cytometry, 82(2), 107-111.

[7]. Baxt, W. G. (1995). Application of artificial neural networks (ANNs) to clinical medicine, Lancet, 346 (8983) 1135-1138. https://doi.org/10.1016/S0140-6736(95) 91804-3

[8]. Bugg, C. W., & Wand, T. (2016). Taira Emerg Med Pract, 18, 1-24. Epub 1 Dec 2016.

[9]. Catalogna, M., Cohen, E., Fishman, S., Halpern, Z., Nevo, U., & Ben-Jacob, E. (2012). Artificial Neural Networks based controller for glucose monitoring during clamp test. PloS One, 7(8), e44587.

[10]. Dayal, S., Singh, A., Chaturvedi, V., Krishna, M., & Gupta, V. (2016). Pattern of Pelvic Inflammatory Disease in women who attended the tertiary care hospital among the rural population of North India. Muller Journal of Medical Sciences and Research, 7(2), 100-104.

[11]. de Canete, J. F., Gonzalez-Perez, S., & Ramos-Diaz, J. C. (2012). Artificial Neural Networks for closed loop control of in silico and ad hoc type 1 diabetes. Computer Methods and Programs in Biomedicine, 106(1), 55-66.

[12]. Dey, P., Lamba, A., Kumari, S., & Marwaha, N. (2012). Application of an artificial neural network in the prognosis of chronic myeloid leukemia. Analytical and Quantitative Cytology and Histology, 33(6), 335-339.

[13]. Elveren, E., & Yumuşak, N. (2011). Tuberculosis disease diagnosis using artificial neural network trained with genetic algorithm. Journal of Medical Systems, 35(3), 329-332.

[14]. Higuera, V. (2016). Pelvic Inflammatory Disease. In Healthline. Retrieved from http://www.healthline.com/ health/pelvic-inflammatory-disease-pid#overview1

[15]. Jefferson, M. F., Pendleton, N., Lucas, S. B., & Horan, M. A. (1997). Comparison of a genetic algorithm neural network with logistic regression for predicting outcome after surgery for patients with nonsmall cell lung carcinoma. Cancer: Interdisciplinary International Journal of the American Cancer Society, 79(7), 1338- 1342.

[16]. Mahesh, C., & Manjula, B. (2013). Diagnosing hepatitis B using Artificial Neural Network based expert system. International Journal of Engineering and Innovative Technology, 3(6) , 139-144.

[17]. Pelvic Inflammatory Disease (PID). Centre for Disease Control (CDC) Fact Sheet. American Social Health Association (ASHA), 2014, pp 1

[18]. Pipa, O., Sally, K., Adamma, A., Helen, A., Sima, H., David, T., Ian, S., & Phillip, H. (2010). Randomised Controlled Trial of Screening for chlamydia trachomatis to prevent Pelvic Inflammatory Disease: The POPI (Prevention of Pelvic Infection) Trial Article.

[19]. Polat, K., & Güneş, S. (2007). Breast cancer diagnosis using least square Support Vector Machine. Digital Signal Processing, 17(4), 694 701. https://doi.org/10.1016/ j.dsp.2006.10.008.

[20]. Price, M. J., Ades, A. E., De Angelis, D., Welton, N. J., Macleod, J., Soldan, K., ... & Horner, P. J. (2013). Risk of Pelvic Inflammatory Disease following Chlamydia trachomatis infection: Analysis of prospective studies with a multistate model. American Journal of Epidemiology, 178(3), 484-492.

[21]. Price, M. J., Ades, A. E., Welton, N. J., Simms, I., Macleod, J., & Horner, P. J. (2016). Proportion of pelvic inflammatory disease cases caused by Chlamydia trachomatis: consistent picture from different methods. The Journal of Infectious Diseases, 214(4), 617-624.

[22]. Rekart, M. L., Gilbert, M., Meza, R., Kim, P. H., Chang, M., Money, D. M., & Brunham, R. C. (2012). Chlamydia public health programs and the epidemiology of Pelvic Inflammatory Disease and ectopic pregnancy. The Journal of Infectious Diseases, 207(1), 30-38.

[23]. Saba, T., Al-Zahrani, S., & Rehman, A. (2012). Expert system for offline clinical guidelines and treatment. Life Science Journal, 9(4), 2639-2658.

[24]. Schindlbeck, C., Dziura, D., & Mylonas, I. (2014). Diagnosis of Pelvic Inflammatory Disease (PID): intraoperative findings and comparison of vaginal and intraabdominal cultures. Archives of Gynecology and Obstetrics, 289(6), 1263-1269.

[25]. Shannon, W., & Sarah, M. (2018). Pelvic Inflammatory Disease. Royal Australian and New Zealand College of Obstetricians and Gynaecologists, East Melbourne Victoria, 3002, Australia, 2018.

[26]. Shanthi, D., Sahoo, G., & Saravanan, N. (2009). Designing an Artificial Neural Network model for the prediction of thrombo-embolic stroke. International Journals of Biometric and Bioinformatics (IJBB), 3(1), 10- 18.

[27]. Shi, L., Wang, X. C., & Wang, Y. S. (2013). Artificial Neural Network models for predicting 1-year mortality in elderly patients with intertrochanteric fractures in China. Brazilian Journal of Medical and Biological Research, 46(11), 993-999. http://dx.doi.org/10.1590/1414- 431X20132948

[28]. Trent, M., (2013). Pelvic Inflammatory Disease, Pediatrics in Review, 34(4), 163-172.

[29]. Visa, S., Ramsay, B., Ralescu, A. L., & Van Der Knaap, E. (2011). Confusion Matrix-based Feature Selection. MAICS, 710, 120-127.

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