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Evaluation of artificial neural network and adaptive-network-based fuzzy inference system for ovarian and lung cancer prediction

Yıl 2024, Cilt: 7 Sayı: 1, 80 - 88, 15.01.2024

Semih Latif İpek Dilek Göktürk

https://doi.org/10.32322/jhsm.1360782

Öz

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.

Anahtar Kelimeler

ANN ANFIS cancer prediction

Kaynakça

  • Nayak M, Das S, Bhanja U, Senapati MR. Elephant herding optimization technique based neural network for cancer prediction. Inform Med Unlocked. 2020;21:100445. doi: 10.1016/j.imu.2020.100445
  • Kumbasar U, Dikmen ZG, Yılmaz Y, Ancın B, Dikmen E, Dogan R. Serum human epididymis protein 4 (HE4) as a diagnostic and follow-up biomarker in patients with non-small cell lung cancer. Int J Hematol Oncol. 2017;27(3):137-142. doi: 10.4999/uhod.171830
  • Ferraro S, Braga F, Lanzoni M, Boracchi P, Biganzoli EM, Panteghini M. Serum human epididymis protein 4 vs carbohydrate antigen 125 for ovarian cancer diagnosis: a systematic review. J Clin Pathol. 2013;66(4):273-281. doi: 10.1136/jclinpath-2012-201031
  • Zhen S, Bian LH, Chang LL, Gao X. Comparison of serum human epididymis protein 4 and carbohydrate antigen 125 as markers in ovarian cancer: a meta‑analysis. Mol Clin Oncol. 2014;2(4):559-566. doi: 10.3892/mco.2014.279
  • Li F, Tie R, Chang K, et al. Does risk for ovarian malignancy algorithm excel human epididymis protein 4 and CA125 in predicting epithelial ovarian cancer: a meta-analysis. BMC Cancer. 2012;12(1):1-18. doi: 10.1186/1471-2407-12-258
  • Drapkin R, Von Horsten HH, Lin Y, et al. Human epididymis protein 4 (HE4) is a secreted glycoprotein that is overexpressed by serous and endometrioid ovarian carcinomas. Cancer Res. 2005;65(6):2162-2169. doi: 10.1158/0008-5472.CAN-04-3924
  • Sørensen SS, Mosgaard BJ. Combination of cancer antigen 125 and carcinoembryonic antigen can improve ovarian cancer diagnosis. Dan Med Bull. 2011;58(11):A4331.
  • Zhu L, Zhuang H, Wang H, et al. Overexpression of HE4 (human epididymis protein 4) enhances proliferation, invasion and metastasis of ovarian cancer. Oncotarget. 2016;7(1):729-744. doi: 10.18632/oncotarget.6327
  • Bolstad N, Øijordsbakken M, Nustad K, Bjerner J. Human epididymis protein 4 reference limits and natural variation in a Nordic reference population. Tumor Biol. 2012;33(1):141-148. doi: 10.1007/s13277-011-0256-4
  • Ribeiro JR, Gaudet HM, Khan M, et al. Human epididymis protein 4 promotes events associated with metastatic ovarian cancer via regulation of the extracelluar matrix. Front Oncol. 2018;7:332. doi: 10.3389/fonc.2017.00332
  • Bashizadeh-Fakhar H, Rezaie-Tavirani M, Zali H, Faraji R, Kazem Nejad E, Aghazadeh M. The diagnostic value of serum CEA, CA-125, and ROMA Index in low-grade serous ovarian cancer. Int J Cancer Manag. 2018;11(5):e63397. doi:10.5812/ijcm.63397
  • Dochez V, Randet M, Renaudeau C, et al. Efficacy of HE4, CA125, risk of malignancy index and risk of ovarian malignancy index to detect ovarian cancer in women with presumed benign ovarian tumours: a prospective, multicentre trial. J Clin Med. 2019;8(11):1784. doi: 10.3390/jcm8111784
  • Dai HY, Hu F, Ding Y. Diagnostic value of serum human epididymis protein 4 and cancer antigen 125 in the patients with ovarian carcinoma: a protocol for systematic review and meta-analysis. Medicine. 2021;100(21):1-4. doi: 10.1097/MD.0000000000025981
  • Huang S, Yang J, Fong S, Zhao Q. Artificial intelligence in cancer diagnosis and prognosis: opportunities and challenges. Cancer Lett. 2020;471:61-71. doi:10.1016/j.canlet.2019.12.007
  • Lu M, Fan Z, Xu B, et al. Using machine learning to predict ovarian cancer. Int J Med Inform. 2020;141:104195. doi: 10.1016/j.ijmedinf.2020.104195
  • Kappen HJ, Neijt JP. Neural network analysis to predict treatment outcome. Ann Oncol. 1993;4:S31-S34. doi: 10.1093/annonc/4.suppl_4.S31
  • Floyd Jr CE, Lo JY, Yun AJ, Sullivan DC, Kornguth PJ. Prediction of breast cancer malignancy using an artificial neural network. Cancer. 1994;74(11):2944-2948.
  • Burke HB, Goodman PH, Rosen DB, et al. Artificial neural networks 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
  • Kim KJ, Cho SB. Prediction of colon cancer using an evolutionary neural network. Neurocomputing. 2004;61:361-379. doi: 10.1016/j.neucom.2003.11.008
  • Saritas I. Prediction of breast cancer using artificial neural networks. J Med Syst. 2012;36(5):2901-2907. doi: 10.1007/s10916-011-9768-0
  • Ecke TH, Bartel P, Hallmann S, et al. Outcome prediction for prostate 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
  • Enshaei A, Robson CN, Edmondson RJ. Artificial intelligence systems as prognostic and predictive tools in ovarian cancer. Ann Surg Oncol. 2015;22(12):3970-3975. doi: 10.1245/s10434-015-4475-6
  • Hambali MA, Gbolagade MD. Ovarian cancer classification using hybrid synthetic minority over-sampling technique and neural network. J Adv Comput Res. 2016;7(4):109-124.
  • Hart GR, Roffman DA, Decker R, Deng J. A multi-parameterized artificial neural network for lung cancer risk prediction. PLoS One. 2018;13(10):e0205264. doi: 10.1371/journal.pone.0205264
  • Charati JY, Janbabaei G, Alipour N, Mohammadi S, Gholiabad SG, Fendereski A. Survival prediction of gastric cancer patients by Artificial Neural Network model. Gastroenterol Hepatol Bed Bench. 2018;11(2):110.
  • NejatZadeh S, Rahimi F, Bardsiri AK, Vahidian E. Predictions of laryngeal cancer using neural network in Kerman Shafa Hospital. Front Health Inform. 2018;7(1):e4.
  • Nasser IM, Abu-Naser SS. Lung cancer detection using artificial neural network. Int J Eng Inf Syst. 2019;3(3):17-23.
  • Daoud M, Mayo M. A survey of neural network-based cancer prediction models from microarray data. Artif Intell Med. 2019;97:204-214. doi: 10.1016/j.artmed.2019.01.006
  • Takeuchi T, Hattori-Kato M, Okuno Y, Iwai S, Mikami K. Prediction of prostate cancer by deep learning with multilayer artificial neural network. Can Urol Assoc J. 2019;13(5):E145. doi: 10.5489/cuaj.5526
  • Muhammad W, Hart GR, Nartowt B, et al. Pancreatic cancer prediction through an artificial neural network. Front Artif Intell. 2019;2:2. doi: 10.3389/frai.2019.00002
  • Appaji SV, Shankar RS, Murthy KVS, Rao CS. Breast cancer disease prediction with recurrent neural networks (RNN). Int J Ind Eng Prod Res. 2020;31(3):379-386. doi: 10.22068/ijiepr.31.3.379
  • Ma X, Lin W, Wu X, et al. A factorization machine based deep neural network for synergism of cancer drug combinations prediction. 4th International Conference on Pattern Recognition and Artificial Intelligence (PRAI). 2021:176-181. doi: 10.1109/PRAI53619.2021.9551036
  • Prisciandaro E, Sedda G, Cara A, Diotti C, Spaggiari L, Bertolaccini L. Artificial neural networks in lung cancer research: a narrative review. J Clin Med. 2023;12(3):880. doi: 10.3390/jcm12030880
  • Madhu, Kumar R. Edge-based convolutional neural network for improving breast cancer prediction performance. Math Probl Eng. 2021;2021:1-15. doi: 10.1155/2021/6613671
  • Chuang YH, Huang SH, Hung TM, et al. Convolutional neural network for human cancer types prediction by integrating protein interaction networks and omics data. Sci Rep. 2021;11(1):1-10. doi: 10.1038/s41598-021-98814-y
  • Lee HA, Chao LR, Hsu CY. A 10-year probability deep neural network prediction model for lung cancer. Cancers. 2021;13(4):928. doi: 10.3390/cancers13040928
  • Tan TZ, Quek C, Ng GS, Razvi K. Ovarian cancer diagnosis with complementary learning fuzzy neural network. Artif Intell Med. 2008;43(3):207-222. doi: 10.1016/j.artmed.2008.04.003
  • Hamdan H, Garibaldi JM. Adaptive neuro-fuzzy inference system (ANFIS) in modelling breast cancer survival. International Conference on Fuzzy Systems. 2010;1-8. doi: 10.1109/FUZZY.2010.5583997
  • Mahmoudi S, Lahijan BS, Kanan HR. ANFIS-based wrapper model gene selection for cancer classification on microarray gene expression data. 13th Iranian Conference on Fuzzy Systems (IFSC). 2013;1-6. doi: 10.1109/IFSC.2013.6675687
  • Hidayah N, Ramadanti AN, Novitasari DCR. Classification of colon cancer based on hispathological images using adaptive neuro fuzzy inference system (ANFIS). Khazanah Inform. 2023;9(2):162-168. doi: 10.23917/khif.v9i2.17611
  • Ziasabounchi N, Askerzade I. ANFIS based classification model for heart disease prediction. Int J Electr Comput Sci. 2014;14(02):7-12.
  • Kalaiselvi C, Nasira GM. A new approach for diagnosis of diabetes and prediction of cancer using ANFIS. 2014 World Congress on Computing and Communication Technologies. 2014;188-190. doi: 10.1109/WCCCT.2014.66
  • Wang CY, Tsai JT, Fang CH, Lee TF, Chou JH. Predicting survival of 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
  • Rahouma KH, Aly RHM, Hamed HF. Brain cancer diagnosis and prediction based on neural gas network and adaptive neuro fuzzy. Procedia Comput Sci. 2019;163:518-526. doi: 10.1016/j.procs.2019.12.134
  • Uyar K, Ilhan U, Ilhan A, Iseri EI. Breast cancer prediction using neuro-fuzzy systems. 7th International Conference on Electrical and Electronics Engineering (ICEEE). 2020;328-332. doi: 10.1109/ICEEE49618.2020.9102476
  • Mishra P, Bhoi N. Cancer gene recognition from microarray data with manta ray based enhanced ANFIS technique. Biocybern Biomed Eng. 2021;41(3):916-932. doi: 10.1016/j.bbe.2021.06.004
  • Kaggle. Does smoking cause lung cancer. Lung Cancer. https://www.kaggle.com/mysarahmadbhat/lung-cancer. Updated Jan 2021. Acesseed Jun 10 2023.
  • Agrawal S, Agrawal J. Neural network techniques for cancer prediction: a survey. Procedia Comput Sci. 2015;60:769-774. doi:10.1016/j.procs.2015.08.234
  • Madhiarasan M, Louzazni M. Analysis of artificial neural network: architecture, types, and forecasting applications. Int J Electr Comput Eng. 2022;2022:5416722. doi: 10.1155/2022/5416722
  • Puspita ANG, Surjandari I, Kawigraha A, Permatasari NV. Optimization of saprolite ore composites reduction process using artificial neural network (ANN). Procedia Comput Sci. 2019;161:424-432. doi:10.1016/j.procs.2019.11.141
  • Jang JS, Sun CT. Neuro-fuzzy modeling and control. Proc IEEE. 1995;83(3):378-406. doi: 10.1109/5.364486
  • Karaboga D, Kaya E. Adaptive network based fuzzy inference system (ANFIS) training approaches: a comprehensive survey. Artif Intell Rev. 2019;52(4):2263-2293. doi: 10.1007/s10462-017-9610-2
  • Lee HA, Chao LR, Hsu CY. A 10-year probability deep neural network prediction model for lung cancer. Cancers. 2021;13(4):928.
  • Dewangan KK, Dewangan DK, Sahu SP, Janghel R. Breast cancer diagnosis in an early stage using novel deep learning with hybrid optimization technique. Multimed Tools Appl. 2022;81(10):13935-13960. doi: 10.1007/s11042-022-12385-2
  • Faisal MI, Bashir S, Khan ZS, Khan FH. An evaluation of machine learning classifiers and ensembles for early stage prediction of lung cancer. 2018 3rd international conference on emerging trends in engineering, sciences and technology (ICEEST). 2018;1-4.
  • Hassan MM, Hassan MM, Yasmin F, et al. A comparative assessment of machine learning algorithms with the Least Absolute Shrinkage and Selection Operator for breast cancer detection and prediction. Decis Anal J. 2023;7:100245. doi: 10.1016/j.dajour.2023.100245
  • Goel A, Goel AK, Kumar A. The role of artificial neural network and machine learning in utilizing spatial information. Spat Inf Res. 2023;31(3):275-285. doi:10.1007/s41324-022-00494-x
  • Ahmed IE, Mehdi R, Mohamed EA. The role of artificial intelligence in developing a banking risk index: an application of Adaptive Neural Network-Based Fuzzy Inference System (ANFIS). Artif Intell Rev. 2023; 56:13873-13895. doi: 10.1007/s10462-023-10473-9
  • İpek SL, Özdemir MD, Göktürk D. Cytotoxic effect of L-methioninase from Brevibacterium linens BL2 in combination with etoposide against Glioblastoma cells. Appl Sci. 2023;13(16):9382. doi: 10.3390/app13169382

Yıl 2024, Cilt: 7 Sayı: 1, 80 - 88, 15.01.2024

Semih Latif İpek Dilek Göktürk

https://doi.org/10.32322/jhsm.1360782

Öz

Kaynakça

  • Nayak M, Das S, Bhanja U, Senapati MR. Elephant herding optimization technique based neural network for cancer prediction. Inform Med Unlocked. 2020;21:100445. doi: 10.1016/j.imu.2020.100445
  • Kumbasar U, Dikmen ZG, Yılmaz Y, Ancın B, Dikmen E, Dogan R. Serum human epididymis protein 4 (HE4) as a diagnostic and follow-up biomarker in patients with non-small cell lung cancer. Int J Hematol Oncol. 2017;27(3):137-142. doi: 10.4999/uhod.171830
  • Ferraro S, Braga F, Lanzoni M, Boracchi P, Biganzoli EM, Panteghini M. Serum human epididymis protein 4 vs carbohydrate antigen 125 for ovarian cancer diagnosis: a systematic review. J Clin Pathol. 2013;66(4):273-281. doi: 10.1136/jclinpath-2012-201031
  • Zhen S, Bian LH, Chang LL, Gao X. Comparison of serum human epididymis protein 4 and carbohydrate antigen 125 as markers in ovarian cancer: a meta‑analysis. Mol Clin Oncol. 2014;2(4):559-566. doi: 10.3892/mco.2014.279
  • Li F, Tie R, Chang K, et al. Does risk for ovarian malignancy algorithm excel human epididymis protein 4 and CA125 in predicting epithelial ovarian cancer: a meta-analysis. BMC Cancer. 2012;12(1):1-18. doi: 10.1186/1471-2407-12-258
  • Drapkin R, Von Horsten HH, Lin Y, et al. Human epididymis protein 4 (HE4) is a secreted glycoprotein that is overexpressed by serous and endometrioid ovarian carcinomas. Cancer Res. 2005;65(6):2162-2169. doi: 10.1158/0008-5472.CAN-04-3924
  • Sørensen SS, Mosgaard BJ. Combination of cancer antigen 125 and carcinoembryonic antigen can improve ovarian cancer diagnosis. Dan Med Bull. 2011;58(11):A4331.
  • Zhu L, Zhuang H, Wang H, et al. Overexpression of HE4 (human epididymis protein 4) enhances proliferation, invasion and metastasis of ovarian cancer. Oncotarget. 2016;7(1):729-744. doi: 10.18632/oncotarget.6327
  • Bolstad N, Øijordsbakken M, Nustad K, Bjerner J. Human epididymis protein 4 reference limits and natural variation in a Nordic reference population. Tumor Biol. 2012;33(1):141-148. doi: 10.1007/s13277-011-0256-4
  • Ribeiro JR, Gaudet HM, Khan M, et al. Human epididymis protein 4 promotes events associated with metastatic ovarian cancer via regulation of the extracelluar matrix. Front Oncol. 2018;7:332. doi: 10.3389/fonc.2017.00332
  • Bashizadeh-Fakhar H, Rezaie-Tavirani M, Zali H, Faraji R, Kazem Nejad E, Aghazadeh M. The diagnostic value of serum CEA, CA-125, and ROMA Index in low-grade serous ovarian cancer. Int J Cancer Manag. 2018;11(5):e63397. doi:10.5812/ijcm.63397
  • Dochez V, Randet M, Renaudeau C, et al. Efficacy of HE4, CA125, risk of malignancy index and risk of ovarian malignancy index to detect ovarian cancer in women with presumed benign ovarian tumours: a prospective, multicentre trial. J Clin Med. 2019;8(11):1784. doi: 10.3390/jcm8111784
  • Dai HY, Hu F, Ding Y. Diagnostic value of serum human epididymis protein 4 and cancer antigen 125 in the patients with ovarian carcinoma: a protocol for systematic review and meta-analysis. Medicine. 2021;100(21):1-4. doi: 10.1097/MD.0000000000025981
  • Huang S, Yang J, Fong S, Zhao Q. Artificial intelligence in cancer diagnosis and prognosis: opportunities and challenges. Cancer Lett. 2020;471:61-71. doi:10.1016/j.canlet.2019.12.007
  • Lu M, Fan Z, Xu B, et al. Using machine learning to predict ovarian cancer. Int J Med Inform. 2020;141:104195. doi: 10.1016/j.ijmedinf.2020.104195
  • Kappen HJ, Neijt JP. Neural network analysis to predict treatment outcome. Ann Oncol. 1993;4:S31-S34. doi: 10.1093/annonc/4.suppl_4.S31
  • Floyd Jr CE, Lo JY, Yun AJ, Sullivan DC, Kornguth PJ. Prediction of breast cancer malignancy using an artificial neural network. Cancer. 1994;74(11):2944-2948.
  • Burke HB, Goodman PH, Rosen DB, et al. Artificial neural networks 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
  • Kim KJ, Cho SB. Prediction of colon cancer using an evolutionary neural network. Neurocomputing. 2004;61:361-379. doi: 10.1016/j.neucom.2003.11.008
  • Saritas I. Prediction of breast cancer using artificial neural networks. J Med Syst. 2012;36(5):2901-2907. doi: 10.1007/s10916-011-9768-0
  • Ecke TH, Bartel P, Hallmann S, et al. Outcome prediction for prostate 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
  • Enshaei A, Robson CN, Edmondson RJ. Artificial intelligence systems as prognostic and predictive tools in ovarian cancer. Ann Surg Oncol. 2015;22(12):3970-3975. doi: 10.1245/s10434-015-4475-6
  • Hambali MA, Gbolagade MD. Ovarian cancer classification using hybrid synthetic minority over-sampling technique and neural network. J Adv Comput Res. 2016;7(4):109-124.
  • Hart GR, Roffman DA, Decker R, Deng J. A multi-parameterized artificial neural network for lung cancer risk prediction. PLoS One. 2018;13(10):e0205264. doi: 10.1371/journal.pone.0205264
  • Charati JY, Janbabaei G, Alipour N, Mohammadi S, Gholiabad SG, Fendereski A. Survival prediction of gastric cancer patients by Artificial Neural Network model. Gastroenterol Hepatol Bed Bench. 2018;11(2):110.
  • NejatZadeh S, Rahimi F, Bardsiri AK, Vahidian E. Predictions of laryngeal cancer using neural network in Kerman Shafa Hospital. Front Health Inform. 2018;7(1):e4.
  • Nasser IM, Abu-Naser SS. Lung cancer detection using artificial neural network. Int J Eng Inf Syst. 2019;3(3):17-23.
  • Daoud M, Mayo M. A survey of neural network-based cancer prediction models from microarray data. Artif Intell Med. 2019;97:204-214. doi: 10.1016/j.artmed.2019.01.006
  • Takeuchi T, Hattori-Kato M, Okuno Y, Iwai S, Mikami K. Prediction of prostate cancer by deep learning with multilayer artificial neural network. Can Urol Assoc J. 2019;13(5):E145. doi: 10.5489/cuaj.5526
  • Muhammad W, Hart GR, Nartowt B, et al. Pancreatic cancer prediction through an artificial neural network. Front Artif Intell. 2019;2:2. doi: 10.3389/frai.2019.00002
  • Appaji SV, Shankar RS, Murthy KVS, Rao CS. Breast cancer disease prediction with recurrent neural networks (RNN). Int J Ind Eng Prod Res. 2020;31(3):379-386. doi: 10.22068/ijiepr.31.3.379
  • Ma X, Lin W, Wu X, et al. A factorization machine based deep neural network for synergism of cancer drug combinations prediction. 4th International Conference on Pattern Recognition and Artificial Intelligence (PRAI). 2021:176-181. doi: 10.1109/PRAI53619.2021.9551036
  • Prisciandaro E, Sedda G, Cara A, Diotti C, Spaggiari L, Bertolaccini L. Artificial neural networks in lung cancer research: a narrative review. J Clin Med. 2023;12(3):880. doi: 10.3390/jcm12030880
  • Madhu, Kumar R. Edge-based convolutional neural network for improving breast cancer prediction performance. Math Probl Eng. 2021;2021:1-15. doi: 10.1155/2021/6613671
  • Chuang YH, Huang SH, Hung TM, et al. Convolutional neural network for human cancer types prediction by integrating protein interaction networks and omics data. Sci Rep. 2021;11(1):1-10. doi: 10.1038/s41598-021-98814-y
  • Lee HA, Chao LR, Hsu CY. A 10-year probability deep neural network prediction model for lung cancer. Cancers. 2021;13(4):928. doi: 10.3390/cancers13040928
  • Tan TZ, Quek C, Ng GS, Razvi K. Ovarian cancer diagnosis with complementary learning fuzzy neural network. Artif Intell Med. 2008;43(3):207-222. doi: 10.1016/j.artmed.2008.04.003
  • Hamdan H, Garibaldi JM. Adaptive neuro-fuzzy inference system (ANFIS) in modelling breast cancer survival. International Conference on Fuzzy Systems. 2010;1-8. doi: 10.1109/FUZZY.2010.5583997
  • Mahmoudi S, Lahijan BS, Kanan HR. ANFIS-based wrapper model gene selection for cancer classification on microarray gene expression data. 13th Iranian Conference on Fuzzy Systems (IFSC). 2013;1-6. doi: 10.1109/IFSC.2013.6675687
  • Hidayah N, Ramadanti AN, Novitasari DCR. Classification of colon cancer based on hispathological images using adaptive neuro fuzzy inference system (ANFIS). Khazanah Inform. 2023;9(2):162-168. doi: 10.23917/khif.v9i2.17611
  • Ziasabounchi N, Askerzade I. ANFIS based classification model for heart disease prediction. Int J Electr Comput Sci. 2014;14(02):7-12.
  • Kalaiselvi C, Nasira GM. A new approach for diagnosis of diabetes and prediction of cancer using ANFIS. 2014 World Congress on Computing and Communication Technologies. 2014;188-190. doi: 10.1109/WCCCT.2014.66
  • Wang CY, Tsai JT, Fang CH, Lee TF, Chou JH. Predicting survival of 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
  • Rahouma KH, Aly RHM, Hamed HF. Brain cancer diagnosis and prediction based on neural gas network and adaptive neuro fuzzy. Procedia Comput Sci. 2019;163:518-526. doi: 10.1016/j.procs.2019.12.134
  • Uyar K, Ilhan U, Ilhan A, Iseri EI. Breast cancer prediction using neuro-fuzzy systems. 7th International Conference on Electrical and Electronics Engineering (ICEEE). 2020;328-332. doi: 10.1109/ICEEE49618.2020.9102476
  • Mishra P, Bhoi N. Cancer gene recognition from microarray data with manta ray based enhanced ANFIS technique. Biocybern Biomed Eng. 2021;41(3):916-932. doi: 10.1016/j.bbe.2021.06.004
  • Kaggle. Does smoking cause lung cancer. Lung Cancer. https://www.kaggle.com/mysarahmadbhat/lung-cancer. Updated Jan 2021. Acesseed Jun 10 2023.
  • Agrawal S, Agrawal J. Neural network techniques for cancer prediction: a survey. Procedia Comput Sci. 2015;60:769-774. doi:10.1016/j.procs.2015.08.234
  • Madhiarasan M, Louzazni M. Analysis of artificial neural network: architecture, types, and forecasting applications. Int J Electr Comput Eng. 2022;2022:5416722. doi: 10.1155/2022/5416722
  • Puspita ANG, Surjandari I, Kawigraha A, Permatasari NV. Optimization of saprolite ore composites reduction process using artificial neural network (ANN). Procedia Comput Sci. 2019;161:424-432. doi:10.1016/j.procs.2019.11.141
  • Jang JS, Sun CT. Neuro-fuzzy modeling and control. Proc IEEE. 1995;83(3):378-406. doi: 10.1109/5.364486
  • Karaboga D, Kaya E. Adaptive network based fuzzy inference system (ANFIS) training approaches: a comprehensive survey. Artif Intell Rev. 2019;52(4):2263-2293. doi: 10.1007/s10462-017-9610-2
  • Lee HA, Chao LR, Hsu CY. A 10-year probability deep neural network prediction model for lung cancer. Cancers. 2021;13(4):928.
  • Dewangan KK, Dewangan DK, Sahu SP, Janghel R. Breast cancer diagnosis in an early stage using novel deep learning with hybrid optimization technique. Multimed Tools Appl. 2022;81(10):13935-13960. doi: 10.1007/s11042-022-12385-2
  • Faisal MI, Bashir S, Khan ZS, Khan FH. An evaluation of machine learning classifiers and ensembles for early stage prediction of lung cancer. 2018 3rd international conference on emerging trends in engineering, sciences and technology (ICEEST). 2018;1-4.
  • Hassan MM, Hassan MM, Yasmin F, et al. A comparative assessment of machine learning algorithms with the Least Absolute Shrinkage and Selection Operator for breast cancer detection and prediction. Decis Anal J. 2023;7:100245. doi: 10.1016/j.dajour.2023.100245
  • Goel A, Goel AK, Kumar A. The role of artificial neural network and machine learning in utilizing spatial information. Spat Inf Res. 2023;31(3):275-285. doi:10.1007/s41324-022-00494-x
  • Ahmed IE, Mehdi R, Mohamed EA. The role of artificial intelligence in developing a banking risk index: an application of Adaptive Neural Network-Based Fuzzy Inference System (ANFIS). Artif Intell Rev. 2023; 56:13873-13895. doi: 10.1007/s10462-023-10473-9
  • İpek SL, Özdemir MD, Göktürk D. Cytotoxic effect of L-methioninase from Brevibacterium linens BL2 in combination with etoposide against Glioblastoma cells. Appl Sci. 2023;13(16):9382. doi: 10.3390/app13169382
Toplam 59 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular Karar Desteği ve Grup Destek Sistemleri
Bölüm Orijinal Makale
Yazarlar

Semih Latif İpek 0000-0002-4661-7765

Dilek Göktürk 0000-0002-1195-5828

Erken Görünüm Tarihi 15 Ocak 2024
Yayımlanma Tarihi 15 Ocak 2024
Yayımlandığı Sayı Yıl 2024 Cilt: 7 Sayı: 1

Kaynak Göster

AMA 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 /JHSM /jhsm. Ocak 2024;7(1):80-88. doi:10.32322/jhsm.1360782
 Kapak Resmi İndir

Üniversitelerarası Kurul (ÜAK) Eşdeğerliği:  Ulakbim TR Dizin'de olan dergilerde yayımlanan makale [10 PUAN] ve 1a, b, c hariç  uluslararası indekslerde (1d) olan dergilerde yayımlanan makale [5 PUAN]

Dahil olduğumuz İndeksler (Dizinler) ve Platformlar sayfanın en altındadır.

Not: Dergimiz WOS indeksli değildir ve bu nedenle Q olarak sınıflandırılmamıştır.

Yüksek Öğretim Kurumu (YÖK) kriterlerine göre yağmacı/şüpheli dergiler hakkındaki kararları ile yazar aydınlatma metni ve dergi ücretlendirme politikasını tarayıcınızdan indirebilirsiniz. https://dergipark.org.tr/tr/journal/2316/file/4905/show 


Dergi Dizin ve Platformları

Dizinler; ULAKBİM TR Dizin, Index Copernicus, ICI World of Journals, DOAJ, Directory of Research Journals Indexing (DRJI), General Impact Factor, ASOS Index, WorldCat (OCLC), MIAR, EuroPub, OpenAIRE, Türkiye Citation Index, Türk Medline Index, InfoBase Index, Scilit, vs.

Platformlar; Google Scholar, CrossRef (DOI), ResearchBib, Open Access, COPE, ICMJE, NCBI, ORCID, Creative Commons vs.