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All Journal International Journal of Multidisciplinary Sciences and Arts BULLET : Jurnal Multidisiplin Ilmu
Shah, Heta Hemang
Unknown Affiliation
Humanities Computer Science & IT Economics, Econometrics & Finance Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Medicine & Pharmacology Nursing

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Advancements in Machine Learning Algorithms: Creating a New Era of Professional Predictive Analytics for Increased Effectiveness of Decision Making Shah, Heta Hemang
BULLET : Jurnal Multidisiplin Ilmu Vol. 3 No. 3 (2024): BULLET : Jurnal Multidisiplin Ilmu
Publisher : CV. Multi Kreasi Media

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Abstract

Forecasting (predictive) analytics has been empowered by machine learning (ML) as a discipline leading to the development of potent instruments for outcome prediction and decision-making enhancement across many domains, such as healthcare, finance, retailing, manufacturing, and transportation. This paper looks at how and what the new advanced ML algorithms in predictive analytics entail and its important applications, and case studies that show its relevance in business areas and domains. Nevertheless, the broader adoption of ML in risk analytics has its challenges and restriction. These are data issues, model issues, model’s interpretability and over fitting, and lastly the issues to do with computational resource needed for model’s development and implementation. Other issues for concern include bias, discrimination, and privacy matters all of which must be taken into account to the enable the proper implementation of the ML technologies. However, model maintenance and scalability are issues because no one model stays great or optimal indefinitely. In conclusion, the article asserts that despite the fact that ML uncovers great possibilities of optimum use to foster efficiency gains, overcoming these issues needs more than a fix; it entails the improved way of data management the ML explain ability initiatives, the ethical regulation of governance and constant model refinement. It is now crucial to overcome these barriers while providing organizations with justified, transparent, and effective use of ML for predictive analytics.
Early Disease Detection through Data Analytics: Turning Healthcare Intelligence Shah, Heta Hemang
International Journal of Multidisciplinary Sciences and Arts Vol. 2 No. 4 (2023): International Journal of Multidisciplinary Sciences and Arts, Article October 2
Publisher : Information Technology and Science (ITScience)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/ijmdsa.v2i2.2995

Abstract

The incorporation of data analytics into disease diagnosis is still in its primitive stage however, its capability to enhance efficiency and patients’ health is tremendously exciting. The use of artificial intelligence (AI), machine learning, wearable technologies, and predictions analyzed can make healthcare systems enable early diagnosis of diseases and cost less to develop preventative health care. However, the change brings several issues of ethical consideration into the equation. Some of such challenges comprise of privacy and confidentiality of sensitive health information, challenges in getting consent and ownership of the patient data. Furthermore, this work discussed that algorithmic bias issue might expand the health disparities if solutions are not found, thus exacerbating inequalities in of health care treatment. The continued development of added AI tools should also be met with the preservation of trust in the doctor and the patient to prevent the man-made intelligence to lead into a replacement of human cognition. For these technologies to benefit the greatest number of people, the advanced technology solutions must be made readily available to avert further widening of the inequalities in health. These ethical questions are best understood and therefore shall be discussed in this paper, with a focus on privacy, consent, fairness, and accessibility. In assessing these issues, the concern of concern can use the big data analytics to enhance the illness diagnosis without violating the patient’s privileges and/or discriminative the equal rights of health care.
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