Abstract

The main reason for the study of the concept is to ensure that the assessment of machine learning and cyber security is facilitated. Technological advances have also resulted in the need for data collection and analysis. The collection of information from multiple sources has been enabled in businesses based on cyber security and machine learning ventures (Manasrah et al., 2021). Security of data is the other critical venture of analyzing the research topic in healthcare. It ensures that the desired measures of managing data and gaining quality service promotion are reached. Health systems have been advanced to ensure that electronic data management is guaranteed.

The concept being assessed also focuses on the programming languages and the importance of the facilitation of the security of data in organizations. Python is one of the programming languages which has developed the decision trees to engage any weaknesses and threats in companies. It has also evaluated random structures for the machine-learning algorithms in place (Manasrah et al., 2021). Production of quality goods and services in healthcare has been reached with the assessment of cyber security and machine learning. Third-party ventures such as those of the attackers have also been limited in healthcare with the assessment of machine learning. Health rights and information protection for patients have also been improved with technological advances.

Bibliography citations

Manasrah, A., Alkayem, A., Qasaimeh, M., & Nofal, S. (2021). Assessment of Machine Learning Security: The Case of Healthcare Data. In International Conference on Data Science, E-learning and Information Systems 2021 (pp. 91-98).

Authors

• • Anood Manasrah, Princess Sumaya for the University of Technology in Jordan.
  • Aisha Alkayem, Princess Sumaya University for Technology.
  • Malik Qasaimeh, Jordan University of Science and technology.
  • Samer Nofai, GUJ, Jordan.

Research concern

With technological advances and the use of social media platforms across the globe, the use of machine learning in the healthcare sector has increased. The collection and analysis of enormous amounts of patient data have been reached in healthcare with the evaluation of machine and deep learning prospects. Over the past years, the study has integrated 769 records of pregnant diabetics to ensure that reviews of their health were engaged (Manasrah et al., 2021). Many concerns have also been noted with the assessment of the security of patient data in healthcare. Most of the hackers are using the technologies in place to gain access to the databases in the health centers without authorization or verification. Change in data accuracy is another crucial venture that has been noted with the analysis of cybersecurity and machine learning in healthcare.

The purpose statement of the research

Assessment of data and promotion of security in healthcare is one of the ventures that have been noted in the companies with analysis of machine learning and cyber security. Cyber security is a technological advancement that integrates data security through the machine and deep learning ventures. Growth and development are crucial aspects that can only be analyzed in businesses through healthy structural technology. The study is also crucial in enhancing the security of the confidential data of patients (Manasrah et al., 2021). Since most of the attackers engage in changing patients’ data in health centers, assessment of cyber security with machine learning is a crucial approach. Deep and machine learning has incorporated human data in ensuring that evaluation of efficiency and effectiveness in the storage of information is enabled.

Record keeping is another venture that has been significantly engaged by the company. It has also allowed the promotion of safety systems to healthcare in enhancing security against any unauthorized personnel. Improving the performance of a model and technology requires the evaluation of machines through the deep learning aspect (Manasrah et al., 2021). Decision-making and communication ventures are some of the critical aspects that cyber security and machine learning enhance in the health sector. In this case, it allows the use of decision trees and random forests in creating accurate learning algorithms.

• How can machine learning improve cyber security?
• How does the healthcare sector facilitate the assessment of cyber security with machine learning?
• What are some of the cyber security demerits and merits in healthcare?
• Does the evaluation of technology promote communication and decision-making?

Precedent literature

Prior to the research on cyber security with machine learning in healthcare, different angles have been structured to ensure that a descriptive research venture is evaluated in the companies. Engagement of personnel training in the healthcare sector is one of the crucial aspects which can promote technological advancements and limit third-party evaluation. Natural language processing venture has also been created by machine learning to ensure that descriptive cyber security is generated. It has also allowed proper programming ventures which can ensure excellence in quality of service provision in the health sector (Manasrah et al., 2021). The accuracy of the stored patients’ data has been attained with the analysis of python. It has also structured desired decision trees to enable proper opinion-making strategies in the companies. Production of quality goods and services can be analyzed in the companies when the assessment of decision-making is promoted.

Research Methodology

Quantitative and qualitative methodologies were utilized in the research to ensure that accurate results were enabled. In this case, a health system was studied in healthcare in Jordan, and it collected 769 records of pregnant diabetics. In the case of the qualitative aspect, interviews and the use of questionnaires were structured to ensure that the patients were conversant with the research being held (Manasrah et al., 2021). The 769 records collected enhance quantitative methodology, which evaluates figures in promoting research assessments. The methodologies engaged also enhanced the use of programming languages such as that python to ensure that accurate data was collected.

Instrumentation

The research was divided into various categories for the collection of accurate results. In this case, the inclusion of the health rights and privacy concerns of the patients, interviews, and assessment of data had to be critical and was managed through cybersecurity with machine learning (Manasrah et al., 2021). Pages (91-98) were utilized to ensure that the quality of the research had been maintained. With the evaluation of the different sections of the research paper, the studies made were accurate and valuable in the companies. Machine learning algorithms also facilitated the achievement of quality data in the health sector.

Findings

The research showcased the importance of machine learning in cybersecurity based on data collection. Machine learning engages efficiency and effectiveness in assessing data collection to be utilized in cybersecurity (Manasrah et al., 2021). Machine learning has also been evaluated in healthcare through cybersecurity since the assessment of algorithms and security ventures are desired. One of the critical demerits that can be noted with cybersecurity is that firewalls challenge arrangements and assessments.

Conclusion

The use of social media platforms and internet prospects have raised the collection of data in companies. For the health sector, the evaluation of cyber security with machine learning is one of the major ventures which has evaluated growth and development (Manasrah et al., 2021). Python is one of the machine learning attributes that have been used in structuring the decision trees for validation of security measures in healthcare. The main goal of the study was to reduce any weaknesses and threats that were faced by the patients’ data on servers