150 related articles for article (PubMed ID: 38303093)
1. Assessing risk factors for malnutrition among women in Bangladesh and forecasting malnutrition using machine learning approaches.
Turjo EA; Rahman MH
BMC Nutr; 2024 Feb; 10(1):22. PubMed ID: 38303093
[TBL] [Abstract][Full Text] [Related]
2. Folic acid supplementation and malaria susceptibility and severity among people taking antifolate antimalarial drugs in endemic areas.
Crider K; Williams J; Qi YP; Gutman J; Yeung L; Mai C; Finkelstain J; Mehta S; Pons-Duran C; Menéndez C; Moraleda C; Rogers L; Daniels K; Green P
Cochrane Database Syst Rev; 2022 Feb; 2(2022):. PubMed ID: 36321557
[TBL] [Abstract][Full Text] [Related]
3. Machine learning algorithms for predicting malnutrition among under-five children in Bangladesh.
Talukder A; Ahammed B
Nutrition; 2020 Oct; 78():110861. PubMed ID: 32592978
[TBL] [Abstract][Full Text] [Related]
4. Can Predictive Modeling Tools Identify Patients at High Risk of Prolonged Opioid Use After ACL Reconstruction?
Anderson AB; Grazal CF; Balazs GC; Potter BK; Dickens JF; Forsberg JA
Clin Orthop Relat Res; 2020 Jul; 478(7):0-1618. PubMed ID: 32282466
[TBL] [Abstract][Full Text] [Related]
5. Machine learning prediction of nutritional status among pregnant women in Bangladesh: Evidence from Bangladesh demographic and health survey 2017-18.
Begum N; Rahman MM; Omar Faruk M
PLoS One; 2024; 19(5):e0304389. PubMed ID: 38820295
[TBL] [Abstract][Full Text] [Related]
6. Predicting nutritional status for women of childbearing age from their economic, health, and demographic features: A supervised machine learning approach.
Khudri MM; Rhee KK; Hasan MS; Ahsan KZ
PLoS One; 2023; 18(5):e0277738. PubMed ID: 37172042
[TBL] [Abstract][Full Text] [Related]
7. Performance Evaluation of Machine Learning Algorithm for Classification of Unintended Pregnancy among Married Women in Bangladesh.
Hossain MI; Habib MJ; Saleheen AAS; Kamruzzaman M; Rahman A; Roy S; Amit Hasan M; Haq I; Methun MIH; Nayan MIH; Rukonozzaman Rukon M
J Healthc Eng; 2022; 2022():1460908. PubMed ID: 35669979
[TBL] [Abstract][Full Text] [Related]
8. Machine learning models for prediction of double and triple burdens of non-communicable diseases in Bangladesh.
Al-Zubayer MA; Alam K; Shanto HH; Maniruzzaman M; Majumder UK; Ahammed B
J Biosoc Sci; 2024 May; 56(3):426-444. PubMed ID: 38505939
[TBL] [Abstract][Full Text] [Related]
9. Investigate the risk factors of stunting, wasting, and underweight among under-five Bangladeshi children and its prediction based on machine learning approach.
Rahman SMJ; Ahmed NAMF; Abedin MM; Ahammed B; Ali M; Rahman MJ; Maniruzzaman M
PLoS One; 2021; 16(6):e0253172. PubMed ID: 34138925
[TBL] [Abstract][Full Text] [Related]
10. Machine learning algorithm for characterizing risks of hypertension, at an early stage in Bangladesh.
Islam MM; Rahman MJ; Chandra Roy D; Tawabunnahar M; Jahan R; Ahmed NAMF; Maniruzzaman M
Diabetes Metab Syndr; 2021; 15(3):877-884. PubMed ID: 33892404
[TBL] [Abstract][Full Text] [Related]
11. An assessment of random forest technique using simulation study: illustration with infant mortality in Bangladesh.
Rahman A; Hossain Z; Kabir E; Rois R
Health Inf Sci Syst; 2022 Dec; 10(1):12. PubMed ID: 35747767
[TBL] [Abstract][Full Text] [Related]
12. A hybrid ensemble approach to accelerate the classification accuracy for predicting malnutrition among under-five children in sub-Saharan African countries.
Khan MNA; Yunus RM
Nutrition; 2023 Apr; 108():111947. PubMed ID: 36641887
[TBL] [Abstract][Full Text] [Related]
13. Machine learning algorithms for outcome prediction in (chemo)radiotherapy: An empirical comparison of classifiers.
Deist TM; Dankers FJWM; Valdes G; Wijsman R; Hsu IC; Oberije C; Lustberg T; van Soest J; Hoebers F; Jochems A; El Naqa I; Wee L; Morin O; Raleigh DR; Bots W; Kaanders JH; Belderbos J; Kwint M; Solberg T; Monshouwer R; Bussink J; Dekker A; Lambin P
Med Phys; 2018 Jul; 45(7):3449-3459. PubMed ID: 29763967
[TBL] [Abstract][Full Text] [Related]
14. Automated detection and classification of diabetes disease based on Bangladesh demographic and health survey data, 2011 using machine learning approach.
Islam MM; Rahman MJ; Chandra Roy D; Maniruzzaman M
Diabetes Metab Syndr; 2020; 14(3):217-219. PubMed ID: 32193086
[TBL] [Abstract][Full Text] [Related]
15. Predicting risks of low birth weight in Bangladesh with machine learning.
Islam Pollob SMA; Abedin MM; Islam MT; Islam MM; Maniruzzaman M
PLoS One; 2022; 17(5):e0267190. PubMed ID: 35617201
[TBL] [Abstract][Full Text] [Related]
16. Assessing predictive performance of supervised machine learning algorithms for a diamond pricing model.
Kigo SN; Omondi EO; Omolo BO
Sci Rep; 2023 Oct; 13(1):17315. PubMed ID: 37828360
[TBL] [Abstract][Full Text] [Related]
17. Machine Learning Approaches to Predict 6-Month Mortality Among Patients With Cancer.
Parikh RB; Manz C; Chivers C; Regli SH; Braun J; Draugelis ME; Schuchter LM; Shulman LN; Navathe AS; Patel MS; O'Connor NR
JAMA Netw Open; 2019 Oct; 2(10):e1915997. PubMed ID: 31651973
[TBL] [Abstract][Full Text] [Related]
18. Construction of the machine learning-based live birth prediction models for the first in vitro fertilization pregnant women.
Liu X; Chen Z; Ji Y
BMC Pregnancy Childbirth; 2023 Jun; 23(1):476. PubMed ID: 37370040
[TBL] [Abstract][Full Text] [Related]
19. A machine-learning-based algorithm improves prediction of preeclampsia-associated adverse outcomes.
Schmidt LJ; Rieger O; Neznansky M; Hackelöer M; Dröge LA; Henrich W; Higgins D; Verlohren S
Am J Obstet Gynecol; 2022 Jul; 227(1):77.e1-77.e30. PubMed ID: 35114187
[TBL] [Abstract][Full Text] [Related]
20. Application of machine learning approaches for osteoporosis risk prediction in postmenopausal women.
Shim JG; Kim DW; Ryu KH; Cho EA; Ahn JH; Kim JI; Lee SH
Arch Osteoporos; 2020 Oct; 15(1):169. PubMed ID: 33097976
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]
