Saux P, Bauvin P, Raverdy V, Teigny J, Verkindt H, Soumphonphakdy T, Debert M, Jacobs A, Jacobs D, Monpellier V, Lee PC, Lim CH, Andersson-Assarsson JC, Carlsson L, Svensson PA, Galtier F, Dezfoulian G, Moldovanu M, Andrieux S, Couster J, Lepage M, Lembo E, Verrastro O, Robert M, Salminen P, Mingrone G, Peterli R, Cohen RV, Zerrweck C, Nocca D, Le Roux CW, Caiazzo R, Preux P, Pattou F (2023) Development and validation of an interpretable machine learning-based calculator for predicting 5-year weight trajectories after bariatric surgery: a multinational retrospective cohort SOPHIA study. Lancet Digit Health. https://doi.org/10.1016/S2589-7500(23)00135-8

Article  PubMed  Google Scholar 

Czernichow S, Rassy N, Malaab J, Loussikian P, Mebarki A, Khadhar M, Poghosyan T, Fagherrazi G, Carette C, Schück S, Rives-Lange C (2023) Patients’ and caregivers’ perceptions of bariatric surgery: a France and United States comparative infodemiology study using social media data mining. Front Digit Health. https://doi.org/10.3389/fdgth.2023.1136326

Article  PubMed  PubMed Central  Google Scholar 

Courcoulas AP, Schauer PR (2019) The surgical management of obesity. In: Brunicardi F, Andersen DK, Billiar TR, Dunn DL, Kao LS, Hunter JG, Matthews JB, Pollock RE (eds) Schwartz’s principles of surgery. McGraw-Hill Education, NY, pp 1167–1218

Google Scholar 

Eisenberg D, Shikora SA, Aarts E, Aminian A, Angrisani L, Cohen RV, de Luca M, Faria SL, Goodpaster KPS, Haddad A, Himpens JM, Kow L, Kurian M, Loi K, Mahawar K, Nimeri A, O’Kane M, Papasavas PK, Ponce J, Pratt JSA, Rogers AM, Steele KE, Suter M, Kothari SN (2023) 2022 American Society of Metabolic and Bariatric Surgery (ASMBS) and International Federation for the Surgery of Obesity and Metabolic Disorders (IFSO) indications for metabolic and bariatric surgery. Obes Surg 33:3–14

Article  PubMed  Google Scholar 

Mechanick JI, Apovian C, Brethauer S, Timothy Garvey W, Joffe AM, Kim J, Kushner RF, Lindquist R, Pessah-Pollack R, Seger J, Urman RD, Adams S, Cleek JB, Correa R, Figaro MK, Flanders K, Grams J, Hurley DL, Kothari S, Seger MV, Still CD (2020) Clinical practice guidelines for the perioperative nutrition, metabolic, and nonsurgical support of patients undergoing bariatric procedures-2019 update: cosponsored by American Association of Clinical Endocrinologists/American College of Endocrinology, The Obesity Society, American Society for Metabolic and Bariatric Surgery, Obesity Medicine Association, and American society of anesthesiologists. Obesity 28:O1-o58

Article  PubMed  Google Scholar 

International Federation for the Surgery of Obesity and Metabolic Disorders (2023) 8th global registry report, September 2023, Available at: https://www.ifso.com/pdf/8th-ifso-registry-report-2023.pdf. Accessed 1 September 2024.

Damhof MA, Pierik E, Krens LL, Vermeer M, van Det MJ, van Roon EN (2019) Assessment of contraceptive counseling and contraceptive use in women after bariatric surgery. Obes Surg 29:4029–4035

Article  PubMed  Google Scholar 

Ciangura C, Coupaye M, Deruelle P, Gascoin G, Calabrese D, Cosson E, Ducarme G, Gaborit B, Lelièvre B, Mandelbrot L, Petrucciani N, Quilliot D, Ritz P, Robin G, Sallé A, Gugenheim J, Nizard J, BARIA-MAT Group (2019) Clinical practice guidelines for childbearing female candidates for bariatric surgery, pregnancy, and post-partum management after bariatric surgery. Obes Surg 29:3722–34

Article  PubMed  Google Scholar 

Moradi R, Kashanian M, Sheidaei A, Kermansaravi M (2024) A systematic review on clinical practice guidelines for managing pregnancy following metabolic-bariatric surgery. Obesity. https://doi.org/10.1002/oby.24118

Article  PubMed  Google Scholar 

Ben Porat T, Yuval JB, Elchalal U, Shushan A, Sakran N, Elazary R, Rottenstreich A (2019) Reproductive health counseling, attitudes, and practices: a cross-sectional survey among bariatric surgeons. Surg Obes Relat Dis 15:2101–2106

Article  PubMed  Google Scholar 

Steyerberg EW (2019) Applications of prediction models. In: Gail M, Samet JM, Singer D (eds) Clinical prediction models: a practical approach to development, validation, and updating. Springer, pp 15–35

Chapter  Google Scholar 

Mahmood SS, Levy D, Vasan RS, Wang TJ (2014) The Framingham heart study and the epidemiology of cardiovascular disease: a historical perspective. Lancet 383:999–1008

Article  PubMed  Google Scholar 

Sheikhtaheri A, Zarkesh MR, Moradi R, Kermani F (2021) Prediction of neonatal deaths in NICUs: development and validation of machine learning models. BMC Med Inform Decis Mak 21:131

Article  PubMed  PubMed Central  Google Scholar 

Kermansaravi M, Shahmiri SS, Khalaj A, Jalali SM, Amini M, Alamdari NM, Mahmoudieh M, Jangjoo A, Abbas SI, Naeini SMM, Sayadishahraki M, Eghbali F, Mirhashemi SH, Mokhber S, Jazi AD, Pazouki A (2022) The first web-based Iranian national obesity and metabolic surgery database (INOSD). Obes Surg 32:2083–2086

Article  PubMed  Google Scholar 

Abdou AM, Wasfy MA, Negm M, Mawla WA, Gertallah LM, Embaby A, Gomaa AF, Sharaf AL, Harb OA, Abdel-Razik AR (2023) Pregnancy after bariatric surgeries; best time, gestational, and neonatal outcomes. Middle East Fertil Soc J (MEFSJ) 28:7

Article  Google Scholar 

Menke MN, King WC, White GE, Gosman GG, Courcoulas AP, Dakin GF, Flum DR, Orcutt MJ, Pomp A, Pories WJ, Purnell JQ, Steffen KJ, Wolfe BM, Yanovski SZ (2019) Conception rates and contraceptive use after bariatric surgery among women with infertility: Evidence from a prospective multicenter cohort study. Surg Obes Relat Dis 15:777–785

Article  PubMed  Google Scholar 

Menke MN, King WC, White GE, Gosman GG, Courcoulas AP, Dakin GF, Flum DR, Orcutt MJ, Pomp A, Pories WJ, Purnell JQ, Steffen KJ, Wolfe BM, Yanovski SZ (2017) Contraception and conception after bariatric surgery. Obstet Gynecol 130:979–987

Article  PubMed  PubMed Central  Google Scholar 

Thornton O, Daggett E, Zia L, Quian A, Close E, Khaitan L, El-Nashar SA, Shaker M (2021) Counseling, contraception, and conception rates in patients undergoing bariatric surgery: a retrospective review. Contraception 104:202–205

Article  PubMed  Google Scholar 

Alam TM, Khan MMA, Iqbal MA, Abdul W, Mushtaq M (2019) Cervical cancer prediction through different screening methods using data mining. Int J Adv Computer Sci Appl (IJACSA), Available at: https://ssrn.com/abstract=3474371. Accessed 14 November 2024.

Douzas G, Bacao F, Last F (2018) Improving imbalanced learning through a heuristic oversampling method based on k-means and SMOTE. Inf Sci 465:1–20

Article  Google Scholar 

Gnanambal S, Thangaraj M, Meenatchi V, Gayathri V (2018) Classification algorithms with attribute selection: an evaluation study using WEKA. Int J Adv Netw Appl (IJANA) 9:3640–3644

Google Scholar 

Bach MP, Zoroja J, Jaković B, Šarlija N (2017, May) Selection of variables for credit risk data mining models: preliminary research. In 2017 40th International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO), pp 1367–72, IEEE.

Renganathan V (2019) Overview of artificial neural network models in the biomedical domain. Bratisl Lek Listy 120:536–540

CAS  PubMed  Google Scholar 

Ma SX, Dhanaliwala AH, Rudie JD, Rauschecker AM, Roberts-Wolfe D, Haddawy P, Kahn CE Jr (2023) Bayesian networks in radiology. Radiol Artif Intell. https://doi.org/10.1148/ryai.210187

Article  PubMed  PubMed Central  Google Scholar 

Chermiti B (2019) Establishing Risk and targeting profiles using dara mining: decision trees. World Customs J (WCJ) 13:39–58

Google Scholar 

Fu R, Tian Y, Shi P, Bao T (2020) Automatic detection of epileptic seizures in EEG using sparse CSP and fisher linear discrimination analysis algorithm. J Med Syst 44:43

Article  PubMed  Google Scholar 

Coz E, Fauvernier M, Maucort-Boulch D (2024) An overview of regression models for adverse events analysis. Drug Saf 47:205–216

Article  PubMed  Google Scholar 

Zhang W, Ji G, Manza P, Li G, Hu Y, Wang J, Lv G, He Y, von Deneen KM, Han Y, Cui G, Tomasi D, Volkow ND, Nie Y, Wang GJ, Zhang Y (2021) Connectome-based prediction of optimal weight loss six months after bariatric surgery. Cereb Cortex 31:2561–2573

Article  PubMed  Google Scholar 

Huang S, Cai N, Pacheco PP, Narrandes S, Wang Y, Xu W (2018) Applications of support vector machine (SVM) learning in cancer genomics. Cancer Genomics Proteomics 15:41–51

CAS  PubMed  Google Scholar 

Zapata-Cortes O, Arango-Serna MD, Zapata-Cortes JA, Restrepo-Carmona JA (2024) Machine learning models and applications for early detection. Sensors. https://doi.org/10.3390/s24144678

Article  PubMed  PubMed Central  Google Scholar 

Charilaou P, Battat R (2022) Machine learning models and over-fitting considerations. World J Gastroenterol 28:605–607

Article  PubMed  PubMed Central  Google Scholar 

Ghasemzadeh H, Hillman RE, Mehta DD (2024) Toward generalizable machine learning models in speech, language, and hearing sciences: estimating sample size and reducing overfitting. J Speech Lang Hear Res 67:753–781

Article  PubMed  PubMed Central  Google Scholar 

Snider EJ, Hernandez-Torres SI, Hennessey R (2023) Using ultrasound image augmentation and ensemble predictions to prevent machine-learning model overfitting. Diagnostics 13:417

Article  PubMed  PubMed Central  Google Scholar 

Chang C, Chang S, Poles J, Popov V (2021) The impact of bariatric surgery compared to metformin therapy on pregnancy outcomes in patients with polycystic ovarian syndrome: a systematic review and meta-analysis. J Gastrointest Surg 25:378–386

Article  PubMed  Google Scholar 

Armanini D, Boscaro M, Bordin L, Sabbadin C (2022) Controversies in the pathogenesis, diagnosis and treatment of PCOS: focus on insulin resistance, inflammation, and hyperandrogenism. Int J Mol Sci 23:4110

Article  CAS