• VENKATESWARANAIDU KOLLURI

The development of machine learning techniques in the clinical diagnosis field has brought about changes in which patient care results could be factored. Hence, there is a need to fully comprehend the full extent that these advancements may have on patient health outcomes. The extensive use of machine learning in the medical facilities, there is no exact measure in quantitative terms that directly confirms or denies the improvement in the wellness of patients resulting from machine learning diagnosis [1]. This brings about our primary objective which is to conduct a comparative analysis involving traditional diagnostic approaches and machine learning approaches, the conceptualisation of patient diagnosis accuracy and resulting outcomes, primarily. This investigation is aimed at helping us realize the fact of the matter by highlighting the true value that can be obtained from machine driven diagnosis in healthcare. One of the objectives of this investigation is to assist physicians in making decisions on incorporating machine learning approaches into their diagnostic procedures[1,2]. A thorough explanation of a balance between the precision diagnosis provided through smart algorithms and the results of the treatment and having in mind besides, the limited available time and resources should be done. It is noteworthy that the problem of diagnostic error has been admitted as an "international problem" in the medical field [2]. Nevertheless, the differences in diagnostic accuracy and resulting outcomes between the different diagnostic procedures remain uncomparable. Thus, the value of our work does not only entail contributing to the understanding of machine learning in medicine, but also it paves the way for future studies in this emerging field. The implications of our research extend far beyond the present moment. They hold the potential to shape the future landscape of machine learning in medicine. By acting as a guiding framework, our findings will provide valuable insights for researchers and practitioners alike, allowing them to navigate the complexities of integrating machine learning into clinical diagnosis effectively [3]. IThis study seeks to fill the existing knowledge gap by comprehensively comparing traditional diagnostic methods with machine learning approaches, thereby illuminating the true worth of machine-derived diagnoses. Through our findings, we endeavor to foster an environment where machine learning can be effectively utilized in clinical settings whilst ensuring optimal patient outcomes.

Predictive analytics, Machine learning, Patient diagnosis, patient diagnosis, accuracy, web search, digital advertisement, healthcare, Wearable devices, Remote monitoring, United States, Electronic health records (EHR)

"THE IMPACT OF MACHINE LEARNING ON PATIENT DIAGNOSIS ACCURACY: INVESTIGATING THE ACCURACY AND EFFICIENCY OF MACHINE LEARNING MODELS IN DIAGNOSING DISEASES", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.11, Issue 1, page no.h402-h406, January-2024, Available :http://www.jetir.org/papers/JETIR2401744.pdf

"THE IMPACT OF MACHINE LEARNING ON PATIENT DIAGNOSIS ACCURACY: INVESTIGATING THE ACCURACY AND EFFICIENCY OF MACHINE LEARNING MODELS IN DIAGNOSING DISEASES", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.11, Issue 1, page no. pph402-h406, January-2024, Available at : http://www.jetir.org/papers/JETIR2401744.pdf