JHSM
Journal of Health Sciences and Medicine

Journal of Health Sciences and Medicine (JHSM) is an unbiased, peer-reviewed, and open access international medical journal. The Journal publishes interesting clinical and experimental research conducted in all fields of medicine, interesting case reports, and clinical images, invited reviews, editorials, letters, comments, and related knowledge.

ISSN: Active
eISSN: 2636-8579
Creative Commons CC BY-NC-ND 4.0 International License.
EndNote Style

Submit Manuscript

Index
Original Article
Evaluation of artificial neural network and adaptive-network-based fuzzy inference system for ovarian and lung cancer prediction
Abstract
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.

Keywords : ANN, ANFIS, cancer prediction

Download Article


1. Nayak M, Das S, Bhanja U, Senapati MR. Elephant herdingoptimization technique based neural network for cancerprediction. Inform Med Unlocked. 2020;21:100445. doi: 10.1016/j.imu.2020.100445
2. Kumbasar U, Dikmen ZG, Yılmaz Y, Ancın B, Dikmen E, DoganR. Serum human epididymis protein 4 (HE4) as a diagnostic andfollow-up biomarker in patients with non-small cell lung cancer.Int J Hematol Oncol. 2017;27(3):137-142. doi: 10.4999/uhod.171830
3. Ferraro S, Braga F, Lanzoni M, Boracchi P, Biganzoli EM, PanteghiniM. Serum human epididymis protein 4 vs carbohydrate antigen125 for ovarian cancer diagnosis: a systematic review. J ClinPathol. 2013;66(4):273-281. doi: 10.1136/jclinpath-2012-201031
4. Zhen S, Bian LH, Chang LL, Gao X. Comparison of serum humanepididymis protein 4 and carbohydrate antigen 125 as markers inovarian cancer: a meta-analysis. Mol Clin Oncol. 2014;2(4):559-566. doi: 10.3892/mco.2014.279
5. Li F, Tie R, Chang K, et al. Does risk for ovarian malignancyalgorithm excel human epididymis protein 4 and CA125 inpredicting epithelial ovarian cancer: a meta-analysis. BMCCancer. 2012;12(1):1-18. doi: 10.1186/1471-2407-12-258
6. Drapkin R, Von Horsten HH, Lin Y, et al. Human epididymisprotein 4 (HE4) is a secreted glycoprotein that is overexpressedby serous and endometrioid ovarian carcinomas. Cancer Res.2005;65(6):2162-2169. doi: 10.1158/0008-5472.CAN-04-3924
7. Sørensen SS, Mosgaard BJ. Combination of cancer antigen 125and carcinoembryonic antigen can improve ovarian cancerdiagnosis. Dan Med Bull. 2011;58(11):A4331.
8. Zhu L, Zhuang H, Wang H, et al. Overexpression of HE4 (humanepididymis protein 4) enhances proliferation, invasion andmetastasis of ovarian cancer. Oncotarget. 2016;7(1):729-744. doi:10.18632/oncotarget.6327
9. Bolstad N, Øijordsbakken M, Nustad K, Bjerner J. Humanepididymis protein 4 reference limits and natural variation in aNordic reference population. Tumor Biol. 2012;33(1):141-148.doi: 10.1007/s13277-011-0256-4
10. Ribeiro JR, Gaudet HM, Khan M, et al. Human epididymis protein4 promotes events associated with metastatic ovarian cancer viaregulation of the extracelluar matrix. Front Oncol. 2018;7:332. doi:10.3389/fonc.2017.00332
11. Bashizadeh-Fakhar H, Rezaie-Tavirani M, Zali H, Faraji R, KazemNejad E, Aghazadeh M. The diagnostic value of serum CEA, CA-125, and ROMA Index in low-grade serous ovarian cancer. Int JCancer Manag. 2018;11(5):e63397. doi:10.5812/ijcm.63397
12. Dochez V, Randet M, Renaudeau C, et al. Efficacy of HE4,CA125, risk of malignancy index and risk of ovarian malignancyindex to detect ovarian cancer in women with presumed benignovarian tumours: a prospective, multicentre trial. J Clin Med.2019;8(11):1784. doi: 10.3390/jcm8111784
13. Dai HY, Hu F, Ding Y. Diagnostic value of serum humanepididymis protein 4 and cancer antigen 125 in the patientswith ovarian carcinoma: a protocol for systematic reviewand meta-analysis. Medicine. 2021;100(21):1-4. doi: 10.1097/MD.0000000000025981
14. Huang S, Yang J, Fong S, Zhao Q. Artificial intelligence in cancerdiagnosis and prognosis: opportunities and challenges. CancerLett. 2020;471:61-71. doi:10.1016/j.canlet.2019.12.007
15. Lu M, Fan Z, Xu B, et al. Using machine learning to predictovarian cancer. Int J Med Inform. 2020;141:104195. doi: 10.1016/j.ijmedinf.2020.104195
16. Kappen HJ, Neijt JP. Neural network analysis to predict treatmentoutcome. Ann Oncol. 1993;4:S31-S34. doi: 10.1093/annonc/4.suppl_4.S31
17. Floyd Jr CE, Lo JY, Yun AJ, Sullivan DC, Kornguth PJ. Predictionof breast cancer malignancy using an artificial neural network.Cancer. 1994;74(11):2944-2948.
18. Burke HB, Goodman PH, Rosen DB, et al. Artificial neuralnetworks improve the accuracy of cancer survival prediction.Cancer. 1997;79(4):857-862. doi: 10.1002/(sici)1097-0142(19970215)79:4<857::aid-cncr24>3.0.co;2-y
19. Kim KJ, Cho SB. Prediction of colon cancer using an evolutionaryneural network. Neurocomputing. 2004;61:361-379. doi: 10.1016/j.neucom.2003.11.008
20. Saritas I. Prediction of breast cancer using artificial neuralnetworks. J Med Syst. 2012;36(5):2901-2907. doi: 10.1007/s10916-011-9768-0
21. Ecke TH, Bartel P, Hallmann S, et al. Outcome prediction forprostate cancer detection rate with artificial neural network(ANN) in daily routine. Urol Oncol. 2012;30(2):139-144. doi:10.1016/j.urolonc.2009.12.009
22. Enshaei A, Robson CN, Edmondson RJ. Artificial intelligencesystems as prognostic and predictive tools in ovarian cancer. AnnSurg Oncol. 2015;22(12):3970-3975. doi: 10.1245/s10434-015-4475-6
23. Hambali MA, Gbolagade MD. Ovarian cancer classification usinghybrid synthetic minority over-sampling technique and neuralnetwork. J Adv Comput Res. 2016;7(4):109-124.
24. Hart GR, Roffman DA, Decker R, Deng J. A multi-parameterizedartificial neural network for lung cancer risk prediction. PLoSOne. 2018;13(10):e0205264. doi: 10.1371/journal.pone.0205264
25. Charati JY, Janbabaei G, Alipour N, Mohammadi S, GholiabadSG, Fendereski A. Survival prediction of gastric cancer patientsby Artificial Neural Network model. Gastroenterol Hepatol BedBench. 2018;11(2):110.
26. NejatZadeh S, Rahimi F, Bardsiri AK, Vahidian E. Predictions oflaryngeal cancer using neural network in Kerman Shafa Hospital.Front Health Inform. 2018;7(1):e4.
27. Nasser IM, Abu-Naser SS. Lung cancer detection using artificialneural network. Int J Eng Inf Syst. 2019;3(3):17-23.
28. Daoud M, Mayo M. A survey of neural network-based cancerprediction models from microarray data. Artif Intell Med.2019;97:204-214. doi: 10.1016/j.artmed.2019.01.006
29. Takeuchi T, Hattori-Kato M, Okuno Y, Iwai S, Mikami K.Prediction of prostate cancer by deep learning with multilayerartificial neural network. Can Urol Assoc J. 2019;13(5):E145. doi:10.5489/cuaj.5526
30. Muhammad W, Hart GR, Nartowt B, et al. Pancreatic cancerprediction through an artificial neural network. Front Artif Intell.2019;2:2. doi: 10.3389/frai.2019.00002
31. Appaji SV, Shankar RS, Murthy KVS, Rao CS. Breast cancer diseaseprediction with recurrent neural networks (RNN). Int J Ind EngProd Res. 2020;31(3):379-386. doi: 10.22068/ijiepr.31.3.379
32. Ma X, Lin W, Wu X, et al. A factorization machine based deepneural network for synergism of cancer drug combinationsprediction. 4th International Conference on Pattern Recognitionand Artificial Intelligence (PRAI). 2021:176-181. doi: 10.1109/PRAI53619.2021.9551036
33. Prisciandaro E, Sedda G, Cara A, Diotti C, Spaggiari L, BertolacciniL. Artificial neural networks in lung cancer research: a narrativereview. J Clin Med. 2023;12(3):880. doi: 10.3390/jcm12030880
34. Madhu, Kumar R. Edge-based convolutional neural network forimproving breast cancer prediction performance. Math Probl Eng.2021;2021:1-15. doi: 10.1155/2021/6613671
35. Chuang YH, Huang SH, Hung TM, et al. Convolutional neuralnetwork for human cancer types prediction by integrating proteininteraction networks and omics data. Sci Rep. 2021;11(1):1-10.doi: 10.1038/s41598-021-98814-y
36. Lee HA, Chao LR, Hsu CY. A 10-year probability deepneural network prediction model for lung cancer. Cancers.2021;13(4):928. doi: 10.3390/cancers13040928
37. Tan TZ, Quek C, Ng GS, Razvi K. Ovarian cancer diagnosis withcomplementary learning fuzzy neural network. Artif Intell Med.2008;43(3):207-222. doi: 10.1016/j.artmed.2008.04.003
38. Hamdan H, Garibaldi JM. Adaptive neuro-fuzzy inference system(ANFIS) in modelling breast cancer survival. InternationalConference on Fuzzy Systems. 2010;1-8. doi: 10.1109/FUZZY.2010.5583997
39. Mahmoudi S, Lahijan BS, Kanan HR. ANFIS-based wrappermodel gene selection for cancer classification on microarray geneexpression data. 13th Iranian Conference on Fuzzy Systems (IFSC).2013;1-6. doi: 10.1109/IFSC.2013.6675687
40. Hidayah N, Ramadanti AN, Novitasari DCR. Classification ofcolon cancer based on hispathological images using adaptiveneuro fuzzy inference system (ANFIS). Khazanah Inform.2023;9(2):162-168. doi: 10.23917/khif.v9i2.17611
41. Ziasabounchi N, Askerzade I. ANFIS based classification modelfor heart disease prediction. Int J Electr Comput Sci. 2014;14(02):7-12.
42. Kalaiselvi C, Nasira GM. A new approach for diagnosis of diabetesand prediction of cancer using ANFIS. 2014 World Congress onComputing and Communication Technologies. 2014;188-190. doi:10.1109/WCCCT.2014.66
43. Wang CY, Tsai JT, Fang CH, Lee TF, Chou JH. Predicting survivalof individual patients with esophageal cancer by adaptive neuro-fuzzy inference system approach. Appl Soft Comput. 2015;35:583-590. doi: 10.1016/j.asoc.2015.05.045
44. Rahouma KH, Aly RHM, Hamed HF. Brain cancer diagnosisand prediction based on neural gas network and adaptive neurofuzzy. Procedia Comput Sci. 2019;163:518-526. doi: 10.1016/j.procs.2019.12.134
45. Uyar K, Ilhan U, Ilhan A, Iseri EI. Breast cancer prediction usingneuro-fuzzy systems. 7th International Conference on Electricaland Electronics Engineering (ICEEE). 2020;328-332. doi: 10.1109/ICEEE49618.2020.9102476
46. Mishra P, Bhoi N. Cancer gene recognition from microarray datawith manta ray based enhanced ANFIS technique. BiocybernBiomed Eng. 2021;41(3):916-932. doi: 10.1016/j.bbe.2021.06.004
47. Kaggle. Does smoking cause lung cancer. Lung Cancer. https://www.kaggle.com/mysarahmadbhat/lung-cancer. Updated Jan2021. Acesseed Jun 10 2023.
48. Agrawal S, Agrawal J. Neural network techniques for cancerprediction: a survey. Procedia Comput Sci. 2015;60:769-774.doi:10.1016/j.procs.2015.08.234
49. Madhiarasan M, Louzazni M. Analysis of artificial neural network:architecture, types, and forecasting applications. Int J ElectrComput Eng. 2022;2022:5416722. doi: 10.1155/2022/5416722
50. Puspita ANG, Surjandari I, Kawigraha A, Permatasari NV.Optimization of saprolite ore composites reduction processusing artificial neural network (ANN). Procedia Comput Sci.2019;161:424-432. doi:10.1016/j.procs.2019.11.141
51. Jang JS, Sun CT. Neuro-fuzzy modeling and control. Proc IEEE.1995;83(3):378-406. doi: 10.1109/5.364486
52. Karaboga D, Kaya E. Adaptive network based fuzzy inferencesystem (ANFIS) training approaches: a comprehensive survey.Artif Intell Rev. 2019;52(4):2263-2293. doi: 10.1007/s10462-017-9610-2
53. Lee HA, Chao LR, Hsu CY. A 10-year probability deepneural network prediction model for lung cancer. Cancers.2021;13(4):928.
54. Dewangan KK, Dewangan DK, Sahu SP, Janghel R. Breast cancerdiagnosis in an early stage using novel deep learning with hybridoptimization technique. Multimed Tools Appl. 2022;81(10):13935-13960. doi: 10.1007/s11042-022-12385-2
55. Faisal MI, Bashir S, Khan ZS, Khan FH. An evaluation of machinelearning classifiers and ensembles for early stage prediction oflung cancer. 2018 3rd international conference on emerging trendsin engineering, sciences and technology (ICEEST). 2018;1-4.
56. Hassan MM, Hassan MM, Yasmin F, et al. A comparativeassessment of machine learning algorithms with the LeastAbsolute Shrinkage and Selection Operator for breast cancerdetection and prediction. Decis Anal J. 2023;7:100245. doi:10.1016/j.dajour.2023.100245
57. Goel A, Goel AK, Kumar A. The role of artificial neural networkand machine learning in utilizing spatial information. Spat InfRes. 2023;31(3):275-285. doi:10.1007/s41324-022-00494-x
58. Ahmed IE, Mehdi R, Mohamed EA. The role of artificialintelligence in developing a banking risk index: an applicationof Adaptive Neural Network-Based Fuzzy Inference System(ANFIS). Artif Intell Rev. 2023; 56:13873-13895. doi: 10.1007/s10462-023-10473-9
59. İpek SL, Özdemir MD, Göktürk D. Cytotoxic effect ofL-methioninase from Brevibacterium linens BL2 incombination with etoposide against Glioblastoma cells. Appl Sci.2023;13(16):9382. doi: 10.3390/app13169382
Article available in :
Volume 7, Issue 1, 2024
Page : 80-88
https://doi.org/10.32322/jhsm.1360782
Article Information
Received : 15 Eyl 2023
Accepted : 25 Ara 2023
Published : 15 Oca 2024
Corresponding Author :

Semih Latif İpek: Department of Food Engineering, Faculty of Engineering, Adana Alparslan Türkeş Science and Technology University, Adana, Turkiye

[email protected]
Citation : İpek SL, Göktürk D. Evaluation of artificial neural network and adaptive-network-based fuzzy inference system for ovarian and lung cancer prediction. J Health Sci Med. 2024;7(1):80-88.

Read: 113
Cited: 0
Download : 120
_Footer