scholarly journals MACHINE LEARNING ALGORITHMS IMPLEMENTATION IN THE HEALTHCARE SYSTEM AS A PROSPECTIVE AREA FOR SCIENCE, HEALTHCARE, AND BUSINESS

2021 ◽  
Vol 17 (3) ◽  
pp. 98-109
Author(s):  
Valerii Vasylevkyi ◽  
Ihor Stepanov ◽  
Roman Koval ◽  
Mariya Soputnyak ◽  
Nataliia Liutianska ◽  
...  
Keyword(s):  
Machine Learning ◽  
Healthcare System ◽  
General Health ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Knowledge Generation ◽  
Limiting Factor ◽  
Biomedical Data ◽  
Biomedical Knowledge ◽  
Diagnosis Of Diseases

Relevance. The current state of medicine is imperfect as in every other field. Some main discrete problems may be separated in diagnostics and disease management. Biomedical data operation difficulties are a serious limiting factor in solving crucial healthcare problems, represented in the statistically significant groups of diseases. Accumulation of life science data creates as possibilities as challenges to effectively utilize it in clinical practice. Machine learning-based tools are necessary for the generation of new insights and the discovery of new hidden patterns especially on big datasets. AI-based decisions may be successfully utilized for diagnosis of diseases, monitoring of general health, prediction of risks, treatment solutions, and biomedical knowledge generation. Objective. To analyze the potential of machine learning algorithms in healthcare on exact existing problems and make a forecast of their development in near future. Method. An analytical review of the literature on keywords from the scientometric databases Scopus, PubMed, Wiley. Search depth 7 years from 2013 to 2020. Results. Analyzing the current general state of the healthcare system we separated the most relevant problems linked to diagnostics, treatment, and systemic management: diagnostics errors, delayed diagnostics (including during emergencies), overdiagnosis, bureaucracy, communication issues, and "handoff" difficulties. We examined details of the convenient decision-making process in the clinical environment in order to define exact points which may be significantly improved by AI-based decisions, among them: diagnosis of diseases, monitoring of general health, prediction of risks, treatment solutions, and biomedical knowledge generation. We defined machine learning algorithms as a prospective tool for disease diagnostics and management, as well as for new utilizable insights generation and big data processing. Conclusion. Machine learning is a group of technologies that can become a cornerstone for dealing with various medical problems. But still, we have some problems to solve before the intense implementation of such tools in the healthcare system.

scholarly journals Performance and efficiency of machine learning algorithms for analyzing rectangular biomedical data

2021 ◽  
Vol 101 (4) ◽  
pp. 430-441 ◽  
Author(s):  
Fei Deng ◽  
Jibing Huang ◽  
Xiaoling Yuan ◽  
Chao Cheng ◽  
Lanjing Zhang
Keyword(s):  
Machine Learning ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Biomedical Data

Forecasting US movies box office performances in Turkey using machine learning algorithms

2020 ◽  
Vol 39 (5) ◽  
pp. 6579-6590
Author(s):  
Sandy Çağlıyor ◽  
Başar Öztayşi ◽  
Selime Sezgin
Keyword(s):  
Machine Learning ◽  
Global Economy ◽  
Learning Algorithms ◽  
Forecast Model ◽  
Machine Learning Algorithms ◽  
Gradient Boosting ◽  
High Stakes ◽  
Box Office ◽  
Industry Forecast ◽  
The Impact

The motion picture industry is one of the largest industries worldwide and has significant importance in the global economy. Considering the high stakes and high risks in the industry, forecast models and decision support systems are gaining importance. Several attempts have been made to estimate the theatrical performance of a movie before or at the early stages of its release. Nevertheless, these models are mostly used for predicting domestic performances and the industry still struggles to predict box office performances in overseas markets. In this study, the aim is to design a forecast model using different machine learning algorithms to estimate the theatrical success of US movies in Turkey. From various sources, a dataset of 1559 movies is constructed. Firstly, independent variables are grouped as pre-release, distributor type, and international distribution based on their characteristic. The number of attendances is discretized into three classes. Four popular machine learning algorithms, artificial neural networks, decision tree regression and gradient boosting tree and random forest are employed, and the impact of each group is observed by compared by the performance models. Then the number of target classes is increased into five and eight and results are compared with the previously developed models in the literature.

Intelligent system of English composition scoring model based on improved machine learning algorithm

2020 ◽  
pp. 1-11
Author(s):  
Jie Liu ◽  
Lin Lin ◽  
Xiufang Liang
Keyword(s):  
Machine Learning ◽  
Evaluation System ◽  
Intelligent System ◽  
Learning Algorithm ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Assessment System ◽  
English Composition ◽  
Region Extraction ◽  
Constraint Model

The online English teaching system has certain requirements for the intelligent scoring system, and the most difficult stage of intelligent scoring in the English test is to score the English composition through the intelligent model. In order to improve the intelligence of English composition scoring, based on machine learning algorithms, this study combines intelligent image recognition technology to improve machine learning algorithms, and proposes an improved MSER-based character candidate region extraction algorithm and a convolutional neural network-based pseudo-character region filtering algorithm. In addition, in order to verify whether the algorithm model proposed in this paper meets the requirements of the group text, that is, to verify the feasibility of the algorithm, the performance of the model proposed in this study is analyzed through design experiments. Moreover, the basic conditions for composition scoring are input into the model as a constraint model. The research results show that the algorithm proposed in this paper has a certain practical effect, and it can be applied to the English assessment system and the online assessment system of the homework evaluation system algorithm system.

The Unlearnable Checkerboard Pattern

2019 ◽  
Vol 1 (2) ◽  
pp. 78-80
Author(s):  
Eric Holloway
Keyword(s):  
Machine Learning ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Checkerboard Pattern ◽  
Simple Task

Detecting some patterns is a simple task for humans, but nearly impossible for current machine learning algorithms.  Here, the "checkerboard" pattern is examined, where human prediction nears 100% and machine prediction drops significantly below 50%.

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