183 related articles for article (PubMed ID: 32149106)
1. Detecting Congestive Heart Failure by Extracting Multimodal Features and Employing Machine Learning Techniques.
Hussain L; Awan IA; Aziz W; Saeed S; Ali A; Zeeshan F; Kwak KS
Biomed Res Int; 2020; 2020():4281243. PubMed ID: 32149106
[TBL] [Abstract][Full Text] [Related]
2. Machine learning based congestive heart failure detection using feature importance ranking of multimodal features.
Hussain L; Aziz W; Khan IR; Alkinani MH; Alowibdi JS
Math Biosci Eng; 2020 Nov; 18(1):69-91. PubMed ID: 33525081
[TBL] [Abstract][Full Text] [Related]
3. Detecting Brain Tumor using Machines Learning Techniques Based on Different Features Extracting Strategies.
Hussain L; Saeed S; Awan IA; Idris A; Nadeem MSA; Chaudhry QU
Curr Med Imaging Rev; 2019; 15(6):595-606. PubMed ID: 32008569
[TBL] [Abstract][Full Text] [Related]
4. Time-domain heart rate variability features for automatic congestive heart failure prediction.
Moses JC; Adibi S; Angelova M; Islam SMS
ESC Heart Fail; 2024 Feb; 11(1):378-389. PubMed ID: 38009405
[TBL] [Abstract][Full Text] [Related]
5. Prostate cancer detection using machine learning techniques by employing combination of features extracting strategies.
Hussain L; Ahmed A; Saeed S; Rathore S; Awan IA; Shah SA; Majid A; Idris A; Awan AA
Cancer Biomark; 2018 Feb; 21(2):393-413. PubMed ID: 29226857
[TBL] [Abstract][Full Text] [Related]
6. Predicting BRAFV600E mutations in papillary thyroid carcinoma using six machine learning algorithms based on ultrasound elastography.
Agyekum EA; Wang YG; Xu FJ; Akortia D; Ren YZ; Chambers KH; Wang X; Taupa JO; Qian XQ
Sci Rep; 2023 Aug; 13(1):12604. PubMed ID: 37537230
[TBL] [Abstract][Full Text] [Related]
7. Detecting epileptic seizure with different feature extracting strategies using robust machine learning classification techniques by applying advance parameter optimization approach.
Hussain L
Cogn Neurodyn; 2018 Jun; 12(3):271-294. PubMed ID: 29765477
[TBL] [Abstract][Full Text] [Related]
8. Machine learning based classification of normal, slow and fast walking by extracting multimodal features from stride interval time series.
Aziz W; Hussain L; Khan IR; Alowibdi JS; Alkinani MH
Math Biosci Eng; 2020 Dec; 18(1):495-517. PubMed ID: 33525104
[TBL] [Abstract][Full Text] [Related]
9. The effect of principal component analysis in the diagnosis of congestive heart failure via heart rate variability analysis.
Selek MB; Yesilkaya B; Egeli SS; Isler Y
Proc Inst Mech Eng H; 2021 Dec; 235(12):1479-1488. PubMed ID: 34365841
[TBL] [Abstract][Full Text] [Related]
10. Automated diagnosis of congestive heart failure using dual tree complex wavelet transform and statistical features extracted from 2s of ECG signals.
Sudarshan VK; Acharya UR; Oh SL; Adam M; Tan JH; Chua CK; Chua KP; Tan RS
Comput Biol Med; 2017 Apr; 83():48-58. PubMed ID: 28231511
[TBL] [Abstract][Full Text] [Related]
11. Congestive heart failure detection using random forest classifier.
Masetic Z; Subasi A
Comput Methods Programs Biomed; 2016 Jul; 130():54-64. PubMed ID: 27208521
[TBL] [Abstract][Full Text] [Related]
12. Leveraging machine learning tools and algorithms for analysis of fruit fly morphometrics.
Salifu D; Ibrahim EA; Tonnang HEZ
Sci Rep; 2022 May; 12(1):7208. PubMed ID: 35505067
[TBL] [Abstract][Full Text] [Related]
13. Computer-Aided Detection of Incidental Lumbar Spine Fractures from Routine Dual-Energy X-Ray Absorptiometry (DEXA) Studies Using a Support Vector Machine (SVM) Classifier.
Mehta SD; Sebro R
J Digit Imaging; 2020 Feb; 33(1):204-210. PubMed ID: 31062114
[TBL] [Abstract][Full Text] [Related]
14. Mild Dehydration Identification Using Machine Learning to Assess Autonomic Responses to Cognitive Stress.
Posada-Quintero HF; Reljin N; Moutran A; Georgopalis D; Lee EC; Giersch GEW; Casa DJ; Chon KH
Nutrients; 2019 Dec; 12(1):. PubMed ID: 31877912
[TBL] [Abstract][Full Text] [Related]
15. Employing phylogenetic tree shape statistics to resolve the underlying host population structure.
Kayondo HW; Ssekagiri A; Nabakooza G; Bbosa N; Ssemwanga D; Kaleebu P; Mwalili S; Mango JM; Leigh Brown AJ; Saenz RA; Galiwango R; Kitayimbwa JM
BMC Bioinformatics; 2021 Nov; 22(1):546. PubMed ID: 34758743
[TBL] [Abstract][Full Text] [Related]
16. Multimodality radiomics prediction of radiotherapy-induced the early proctitis and cystitis in rectal cancer patients: a machine learning study.
Abbaspour S; Barahman M; Abdollahi H; Arabalibeik H; Hajainfar G; Babaei M; Iraji H; Barzegartahamtan M; Ay MR; Mahdavi SR
Biomed Phys Eng Express; 2023 Dec; 10(1):. PubMed ID: 37995359
[No Abstract] [Full Text] [Related]
17. Predicting sustainable arsenic mitigation using machine learning techniques.
Singh SK; Taylor RW; Pradhan B; Shirzadi A; Pham BT
Ecotoxicol Environ Saf; 2022 Mar; 232():113271. PubMed ID: 35121252
[TBL] [Abstract][Full Text] [Related]
18. Development and validation of warning system of ventricular tachyarrhythmia in patients with heart failure with heart rate variability data.
Au-Yeung WM; Reinhall PG; Bardy GH; Brunton SL
PLoS One; 2018; 13(11):e0207215. PubMed ID: 30427880
[TBL] [Abstract][Full Text] [Related]
19. Application of kNN and SVM to predict the prognosis of advanced schistosomiasis.
Zhou X; Wang H; Xu C; Peng L; Xu F; Lian L; Deng G; Ji S; Hu M; Zhu H; Xu Y; Li G
Parasitol Res; 2022 Aug; 121(8):2457-2460. PubMed ID: 35767047
[TBL] [Abstract][Full Text] [Related]
20. Machine learning-based radiomics analysis in predicting RAS mutational status using magnetic resonance imaging.
Granata V; Fusco R; Brunese MC; Di Mauro A; Avallone A; Ottaiano A; Izzo F; Normanno N; Petrillo A
Radiol Med; 2024 Mar; 129(3):420-428. PubMed ID: 38308061
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]
