A machine learning based data modeling for medical diagnosis

Mahoto, Naeem Ahmed; Shaikh, Asadullah; Sulaiman, Adel; Reshan, Mana Saleh Al; Rajab, Adel; Rajab, Khairan

doi:10.1016/j.bspc.2022.104481

Cited by 19 publications

(7 citation statements)

References 16 publications

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“…The way it uses data and its algorithms is definitory as ML does not receive pre-defined inputs from the surrounding environment in order to learn and make predictions [ 7 , 8 ] and is trained to understand data (e.g., images, numbers) and the connection between input variables. The process is based on examples, gained experience, and if given instructions, ML can learn autonomously [ 11 ].…”

Section: Resultsmentioning

confidence: 99%

Artificial Intelligence in the Advanced Diagnosis of Bladder Cancer-Comprehensive Literature Review and Future Advancement

Ferro

Falagario

Barone³

et al. 2023

Diagnostics

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Artificial intelligence is highly regarded as the most promising future technology that will have a great impact on healthcare across all specialties. Its subsets, machine learning, deep learning, and artificial neural networks, are able to automatically learn from massive amounts of data and can improve the prediction algorithms to enhance their performance. This area is still under development, but the latest evidence shows great potential in the diagnosis, prognosis, and treatment of urological diseases, including bladder cancer, which are currently using old prediction tools and historical nomograms. This review focuses on highly significant and comprehensive literature evidence of artificial intelligence in the management of bladder cancer and investigates the near introduction in clinical practice.

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Section: Resultsmentioning

confidence: 99%

Artificial Intelligence in the Advanced Diagnosis of Bladder Cancer-Comprehensive Literature Review and Future Advancement

Ferro

Falagario

Barone³

et al. 2023

Diagnostics

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“…Classical ML algorithms, despite their limitations, offer several advantages in the context of cardiac disease prognosis. Here are some key advantages [20], [21], [22], [23].…”

Section: Advantages Of ML In Prognosis Of Heart Diseasesmentioning

confidence: 99%

Artificial Intelligence and Machine Learning Algorithms in Modern Cardiology

Petreska,

Slavkovska

2024

SEE J Cardiol

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BACKGROUND: Recent years have witnessed the widespread adoption of machine learning (ML) and deep learning techniques in various health-care applications. Artificial intelligence and ML algorithms using big medical data make it possible to predict diseases and enable the development of personalized treatments for patients. Heart diseases are one of the most common chronic diseases affecting human health, and early detection can reduce the mortality rate. AIM: We aimed to review different types of ML techniques and their applications in heart disease risk detection. METHODS: For different cardiovascular diseases, the choice of algorithms should be tailored based on their accuracy and efficiency RESULTS: The research presented highlights the critical global issue of heart disease and its impact on public health. The urgency to address this global problem is emphasized, as heart disease has become a significant factor in the increasing mortality rate worldwide. The introduction of ML in the prognosis of heart disease is a significant step toward realizing predictive, preventive, and personalized health care and reducing health-care costs. In this study, a comparative evaluation of ML models was made: Logistic regression, decision tree, random forest, and support vector machine. The quality of the data, as well as the choice of an appropriate algorithm, is key factors in the assessment of heart diseases. CONCLUSION: Despite the impressive performance of ML, there are doubts about its robustness in traditional health-care systems due to many security and privacy issues.

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“…Additionally, the Selected Features of the medical data are subjected to the same machine learning methods. The second period of the process aids in obtaining predictions that are more accurate than those made in the first period [3].…”

Section: A Machine Learning Based Data Modeling For Medical Diagnosismentioning

confidence: 99%

Modelling methods of artificial intelligence in medical application

Sun

2023

ACE

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Artificial intelligence is a branch of computer science, an intelligent system that can simulate human thinking, recognize complex situations, acquire learning abilities and knowledge, and solve problems. With the continuous development of information technology, artificial intelligence techniques are increasingly being improved and applied to large-scale genetics research, image detection and classification in medicine. Predictive models for medical data can be built using a wide range of machine learning algorithms: decision trees, multilayer perceptrons, plain Bayes, random forests, and support vector machines, etc., thus processing massive, high-dimensional data and conducting medical research. This paper addresses the specific applications of artificial intelligence in medical practice.

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A machine learning based data modeling for medical diagnosis

Cited by 19 publications

References 16 publications

Artificial Intelligence in the Advanced Diagnosis of Bladder Cancer-Comprehensive Literature Review and Future Advancement

Artificial Intelligence in the Advanced Diagnosis of Bladder Cancer-Comprehensive Literature Review and Future Advancement

Artificial Intelligence and Machine Learning Algorithms in Modern Cardiology

Modelling methods of artificial intelligence in medical application

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