The Use of Artificial Intelligence in Disease Diagnosis: A Systematic Literature Review
DOI:
https://doi.org/10.59247/jahir.v1i3.172Keywords:
disease diagnose, Machine Learning, artificial intelligence, deep learning, systematic reviewAbstract
This paper discusses the crucial role of Artificial Intelligence in improving disease diagnosis and the use of medical data in the era of big data. Through the Systematic Literature Review (SLR) approach, we review recent developments in the application of Machine Learning (ML) to disease diagnosis, evaluate the ML techniques applied, and highlight their impact on healthcare. BP-CapsNet uses the Convolutional Capsule Network (CapsNet) to diagnose cancer with advantages in overcoming invariance to image transformation. The Stacking Classifier achieves 92% accuracy in detecting heart defects with the advantage of combining weak learners. CraftNet, a combination of deep learning and handmade features, demonstrates strong recognition capabilities in cardiovascular disease. ML in infectious diseases demonstrates the ability to process big data, focusing on bacterial, viral, and tuberculosis infections. In heart disease diagnosis, ML, especially with CNN and DNN, detects disease at an early stage, despite the challenges of data imbalance. The ensemble algorithm for heart disease prediction demonstrates the superiority of categorical medical features, with SVM and AdaBoost as suitable methods. A new CNN for wide QRS complex tachycardia provides accurate results. In vestibular disease, five ML algorithms provide satisfactory results, with SVM as the best. These findings detail the development of ML in disease diagnosis, highlighting future challenges and opportunities in its use
References
M. A. Kazancigil, ‘Big medical data, cloud computing, and artificial intelligence for improving diagnosis in healthcare’, in Big Data Analytics for Healthcare, Elsevier, 2022, pp. 139–150. doi: 10.1016/B978-0-323-91907-4.00021-2.
O. S. Prova, F. Ahmed, J. Sultana, and Md. Ashrafuzzaman, ‘Big medical data analytics for diagnosis’, in Big Data Analytics for Healthcare, Elsevier, 2022, pp. 111–124. doi: 10.1016/B978-0-323-91907-4.00013-3.
W. Lin, W. Dou, Z. Zhou, and C. Liu, ‘A cloud-based framework for Home-diagnosis service over big medical data’, Journal of Systems and Software, vol. 102, pp. 192–206, Apr. 2015, doi: 10.1016/j.jss.2014.05.068.
Z. Zhu, G. Peng, Y. Chen, and H. Gao, ‘A convolutional neural network based on a capsule network with strong generalization for bearing fault diagnosis’, Neurocomputing, vol. 323, pp. 62–75, Jan. 2019, doi: 10.1016/j.neucom.2018.09.050.
F. Amato, A. López, E. M. Peña-Méndez, P. Vaňhara, A. Hampl, and J. Havel, ‘Artificial neural networks in medical diagnosis’, J Appl Biomed, vol. 11, no. 2, pp. 47–58, Jul. 2013, doi: 10.2478/v10136-012-0031-x.
M. Badawy, N. Ramadan, and H. A. Hefny, ‘Healthcare predictive analytics using machine learning and deep learning techniques: a survey’, Journal of Electrical Systems and Information Technology, vol. 10, no. 1, p. 40, Aug. 2023, doi: 10.1186/s43067-023-00108-y.
Y. Kumar, A. Koul, R. Singla, and M. F. Ijaz, ‘Artificial intelligence in disease diagnosis: a systematic literature review, synthesizing framework and future research agenda’, J Ambient Intell Humaniz Comput, vol. 14, no. 7, pp. 8459–8486, Jul. 2023, doi: 10.1007/s12652-021-03612-z.
K. Swanson, E. Wu, A. Zhang, A. A. Alizadeh, and J. Zou, ‘From patterns to patients: Advances in clinical machine learning for cancer diagnosis, prognosis, and treatment’, Cell, vol. 186, no. 8, pp. 1772–1791, Apr. 2023, doi: 10.1016/J.CELL.2023.01.035.
L. Agnello et al., ‘Machine learning algorithms in sepsis’, Clinica Chimica Acta, vol. 553, p. 117738, Jan. 2024, doi: 10.1016/j.cca.2023.117738.
A. Bakrania, N. Joshi, X. Zhao, G. Zheng, and M. Bhat, ‘Artificial intelligence in liver cancers: Decoding the impact of machine learning models in clinical diagnosis of primary liver cancers and liver cancer metastases’, Pharmacol Res, vol. 189, p. 106706, Mar. 2023, doi: 10.1016/j.phrs.2023.106706.
A. D. Vergaray, J. C. H. Miranda, J. B. Cornelio, A. R. L. Carranza, and C. F. P. Sanchez, ‘Prediction of Heart Disease Using Machine Learning: A Systematic Literature Review’, Journal of System and Management Sciences, vol. 13, no. 6, Nov. 2023, doi: 10.33168/JSMS.2023.0603.
I. Ibrahim and A. Abdulazeez, ‘The Role of Machine Learning Algorithms for Diagnosing Diseases’, Journal of Applied Science and Technology Trends, vol. 2, no. 01, pp. 10–19, Mar. 2021, doi: 10.38094/jastt20179.
L. Yin, Q. Zhang, C. Wu, and Q. Mou, ‘Novel three-way decision model in medical diagnosis based on inexact reasoning’, Eng Appl Artif Intell, vol. 130, p. 107731, Apr. 2024, doi: 10.1016/J.ENGAPPAI.2023.107731.
J. S. Y. Low, T. M. Thevarajah, S. W. Chang, and S. M. Khor, ‘Paper-based multiplexed colorimetric biosensing of cardiac and lipid biomarkers integrated with machine learning for accurate acute myocardial infarction early diagnosis and prognosis’, Sens Actuators B Chem, vol. 394, p. 134403, Nov. 2023, doi: 10.1016/J.SNB.2023.134403.
C.-H. Chang, C.-H. Lin, and H.-Y. Lane, ‘Machine Learning and Novel Biomarkers for the Diagnosis of Alzheimer’s Disease’, Int J Mol Sci, vol. 22, no. 5, p. 2761, Mar. 2021, doi: 10.3390/ijms22052761.
M. Bakator and D. Radosav, ‘Deep Learning and Medical Diagnosis: A Review of Literature’, Multimodal Technologies and Interaction, vol. 2, no. 3, p. 47, Aug. 2018, doi: 10.3390/mti2030047.
K. Arun Bhavsar, J. Singla, Y. D. Al-Otaibi, O.-Y. Song, Y. Bin Zikriya, and A. Kashif Bashir, ‘Medical Diagnosis Using Machine Learning: A Statistical Review’, Computers, Materials & Continua, vol. 67, no. 1, pp. 107–125, 2021, doi: 10.32604/cmc.2021.014604.
W. Hao, S. Liu, C. Lv, Y. Wang, and J. Wang, ‘Efficient and privacy-preserving multi-party skyline queries in online medical primary diagnosis’, Journal of King Saud University - Computer and Information Sciences, vol. 35, no. 8, p. 101637, Sep. 2023, doi: 10.1016/J.JKSUCI.2023.101637.
D. Hu, Y. Wang, G. Ji, and Y. Liu, ‘Using machine learning algorithms to predict the prognosis of advanced nasopharyngeal carcinoma after intensity-modulated radiotherapy’, Curr Probl Cancer, vol. 48, p. 101040, Feb. 2024, doi: 10.1016/J.CURRPROBLCANCER.2023.101040.
M. Mirbabaie, S. Stieglitz, and N. R. J. Frick, ‘Artificial intelligence in disease diagnostics: A critical review and classification on the current state of research guiding future direction’, Health Technol (Berl), vol. 11, no. 4, pp. 693–731, Jul. 2021, doi: 10.1007/s12553-021-00555-5.
H. S. R. Rajula, G. Verlato, M. Manchia, N. Antonucci, and V. Fanos, ‘Comparison of Conventional Statistical Methods with Machine Learning in Medicine: Diagnosis, Drug Development, and Treatment’, Medicina (B Aires), vol. 56, no. 9, p. 455, Sep. 2020, doi: 10.3390/medicina56090455.
P. Roman-Naranjo, A. M. Parra-Perez, and J. A. Lopez-Escamez, ‘A systematic review on machine learning approaches in the diagnosis and prognosis of rare genetic diseases’, J Biomed Inform, vol. 143, p. 104429, Jul. 2023, doi: 10.1016/J.JBI.2023.104429.
J. Yuan et al., ‘Machine learning applications on neuroimaging for diagnosis and prognosis of epilepsy: A review’, J Neurosci Methods, vol. 368, p. 109441, Feb. 2022, doi: 10.1016/J.JNEUMETH.2021.109441.
C. Cooper, A. Booth, J. Varley-Campbell, N. Britten, and R. Garside, ‘Defining the process to literature searching in systematic reviews: a literature review of guidance and supporting studies’, BMC Med Res Methodol, vol. 18, no. 1, p. 85, Dec. 2018, doi: 10.1186/s12874-018-0545-3.
G. Lame, ‘Systematic Literature Reviews: An Introduction’, Proceedings of the Design Society: International Conference on Engineering Design, vol. 1, no. 1, pp. 1633–1642, Jul. 2019, doi: 10.1017/dsi.2019.169.
Y. Lei, Z. Wu, Z. Li, Y. Yang, and Z. Liang, ‘BP-CapsNet: An image-based Deep Learning method for medical diagnosis’, Appl Soft Comput, vol. 146, p. 110683, Oct. 2023, doi: 10.1016/J.ASOC.2023.110683.
S. Mohapatra et al., ‘A stacking classifiers model for detecting heart irregularities and predicting Cardiovascular Disease’, Healthcare Analytics, vol. 3, p. 100133, Nov. 2023, doi: 10.1016/j.health.2022.100133.
N. Peiffer-Smadja et al., ‘Machine learning for clinical decision support in infectious diseases: a narrative review of current applications’, Clinical Microbiology and Infection, vol. 26, no. 5, pp. 584–595, May 2020, doi: 10.1016/j.cmi.2019.09.009.
Y. Li et al., ‘CraftNet: A deep learning ensemble to diagnose cardiovascular diseases’, Biomed Signal Process Control, vol. 62, p. 102091, Sep. 2020, doi: 10.1016/j.bspc.2020.102091.
M. M. Ahsan and Z. Siddique, ‘Machine learning-based heart disease diagnosis: A systematic literature review’, Artif Intell Med, vol. 128, p. 102289, Jun. 2022, doi: 10.1016/j.artmed.2022.102289.
C. Pan, A. Poddar, R. Mukherjee, and A. K. Ray, ‘Impact of categorical and numerical features in ensemble machine learning frameworks for heart disease prediction’, Biomed Signal Process Control, vol. 76, p. 103666, Jul. 2022, doi: 10.1016/j.bspc.2022.103666.
N. Fayyazifar et al., ‘A novel convolutional neural network structure for differential diagnosis of wide QRS complex tachycardia’, Biomed Signal Process Control, vol. 81, p. 104506, Mar. 2023, doi: 10.1016/j.bspc.2022.104506.
D. T. Anh, H. Takakura, M. Asai, N. Ueda, and H. Shojaku, ‘Application of machine learning in the diagnosis of vestibular disease’, Sci Rep, vol. 12, no. 1, p. 20805, Dec. 2022, doi: 10.1038/s41598-022-24979-9.
M. Revilla-León et al., ‘Artificial intelligence applications in restorative dentistry: A systematic review’, J Prosthet Dent, vol. 128, no. 5, pp. 867–875, Nov. 2022, doi: 10.1016/j.prosdent.2021.02.010.
S. Singh et al., ‘Meta-Analysis of the Performance of AI-Driven ECG Interpretation in the Diagnosis of Valvular Heart Diseases’, Am J Cardiol, vol. 213, pp. 126–131, Feb. 2024, doi: 10.1016/j.amjcard.2023.12.015.
Md. I. Pramanik, R. Y. K. Lau, Md. A. K. Azad, Md. S. Hossain, Md. K. H. Chowdhury, and B. K. Karmaker, ‘Healthcare informatics and analytics in big data’, Expert Syst Appl, vol. 152, p. 113388, Aug. 2020, doi: 10.1016/j.eswa.2020.113388.
V. Palanisamy and R. Thirunavukarasu, ‘Implications of big data analytics in developing healthcare frameworks – A review’, Journal of King Saud University - Computer and Information Sciences, vol. 31, no. 4, pp. 415–425, Oct. 2019, doi: 10.1016/j.jksuci.2017.12.007.
S. Kierner, J. Kucharski, and Z. Kierner, ‘Taxonomy of hybrid architectures involving rule-based reasoning and machine learning in clinical decision systems: A scoping review’, J Biomed Inform, vol. 144, p. 104428, Aug. 2023, doi: 10.1016/j.jbi.2023.104428.
L. Adlung, Y. Cohen, U. Mor, and E. Elinav, ‘Machine learning in clinical decision making’, Med, vol. 2, no. 6, pp. 642–665, Jun. 2021, doi: 10.1016/j.medj.2021.04.006.
L. Wang et al., ‘Artificial intelligence in clinical decision support systems for oncology’, Int J Med Sci, vol. 20, no. 1, pp. 79–86, 2023, doi: 10.7150/ijms.77205.
M. I. Miller, L. C. Shih, and V. B. Kolachalama, ‘Machine Learning in Clinical Trials: A Primer with Applications to Neurology’, Neurotherapeutics, vol. 20, no. 4, pp. 1066–1080, Jul. 2023, doi: 10.1007/s13311-023-01384-2.
M. Mirbabaie, S. Stieglitz, and N. R. J. Frick, ‘Artificial intelligence in disease diagnostics: A critical review and classification on the current state of research guiding future direction’, Health Technol (Berl), vol. 11, no. 4, pp. 693–731, Jul. 2021, doi: 10.1007/s12553-021-00555-5.
G. Singh et al., ‘Machine learning in cardiac CT: Basic concepts and contemporary data’, J Cardiovasc Comput Tomogr, vol. 12, no. 3, pp. 192–201, May 2018, doi: 10.1016/j.jcct.2018.04.010.
C. M. Smith, A. L. Weathers, and S. L. Lewis, ‘An overview of clinical machine learning applications in neurology’, J Neurol Sci, vol. 455, p. 122799, Dec. 2023, doi: 10.1016/j.jns.2023.122799.
A. Qayyum, J. Qadir, M. Bilal, and A. Al-Fuqaha, ‘Secure and Robust Machine Learning for Healthcare: A Survey’, IEEE Rev Biomed Eng, vol. 14, pp. 156–180, 2021, doi: 10.1109/RBME.2020.3013489.
C.-C. Lee et al., ‘Clinical Impacts of Delayed Diagnosis of Hirschsprung’s Disease in Newborn Infants’, Pediatr Neonatol, vol. 53, no. 2, pp. 133–137, Apr. 2012, doi: 10.1016/j.pedneo.2012.01.011.
J. T. Anderson, J. Cowan, A. Condino-Neto, D. Levy, and S. Prusty, ‘Health-related quality of life in primary immunodeficiencies: Impact of delayed diagnosis and treatment burden’, Clinical Immunology, vol. 236, p. 108931, Mar. 2022, doi: 10.1016/j.clim.2022.108931.
A. Ilmudeen, ‘Big data-based frameworks for healthcare systems’, in Demystifying Big Data, Machine Learning, and Deep Learning for Healthcare Analytics, Elsevier, 2021, pp. 33–56. doi: 10.1016/B978-0-12-821633-0.00003-9.
F. Navazi, Y. Yuan, and N. Archer, ‘An examination of the hybrid meta-heuristic machine learning algorithms for early diagnosis of type II diabetes using big data feature selection’, Healthcare Analytics, vol. 4, p. 100227, Dec. 2023, doi: 10.1016/j.health.2023.100227.
V. Palanisamy and R. Thirunavukarasu, ‘Implications of big data analytics in developing healthcare frameworks – A review’, Journal of King Saud University - Computer and Information Sciences, vol. 31, no. 4, pp. 415–425, Oct. 2019, doi: 10.1016/j.jksuci.2017.12.007.
K. Yan, T. Li, J. A. L. Marques, J. Gao, and S. J. Fong, ‘A review on multimodal machine learning in medical diagnostics’, Mathematical Biosciences and Engineering, vol. 20, no. 5, pp. 8708–8726, 2023, doi: 10.3934/mbe.2023382.
S. Aminizadeh et al., ‘The applications of machine learning techniques in medical data processing based on distributed computing and the Internet of Things’, Comput Methods Programs Biomed, vol. 241, p. 107745, Nov. 2023, doi: 10.1016/j.cmpb.2023.107745.
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