| Autor: |
Vardhan, P. Vishnu, Reddy, C. Venkata Akhlieswar, Krishna, Ram, Reddy, N. Yogeshwar, Kumar, K. Santhosh, Kumar, B. Sravan, Rao, T. V. V. L. N. |
| Předmět: |
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| Zdroj: |
AIP Conference Proceedings; 2023, Vol. 2715 Issue 1, p1-6, 6p |
| Abstrakt: |
In this paper, machine learning techniques are used to analyze medical data obtained during the early phases of life safety. Diabetes is a life-threatening condition for which there is no cure. A person suffering from this illness will be afflicted by it for the rest of their lives. In addition to this, too much glucose in the blood can cause health problems. Among the medical conditions that can cause damage to the kidneys are kidney diseases, heart diseases, strokes, dental problems, foot problems, and nerve damage. Thus, it is important to take preventative measures to care for one's medical health. It is possible to access a large number of medical datasets in various formats, each partitioned for an analytical objective that is utilized in real-world applications. An example would be the analysis of disease data. Globally, diabetic diseases are becoming one of the leading causes of morality. In contrast, there are other types of diabetes that can occur during pregnancy, such as gestational diabetes. Having gestational diabetes can harm health and the health of unborn child. Diagnoses and treatment of diabetes can be managed by using machine learning and data mining techniques. To organize and analyze symptoms in medical data, different factors were used in various studies at different phases. Based on data from the National Institute of Diabetes and Digestive and Kidney Diseases, this study examined 750 cases. The research examined recent advances in machine learning, which have had a substantial effect on diabetes detection and diagnosis. In this study, machine learning techniques were used to categorize diabetic patients. The accuracy of categorization is achieved through the classification of diabetic patients. K-Nearest Neighbors (KNN) is the most widely used machine learning prediction method. This study employed the KNN algorithm methodology to identify the degree of health condition and performance as well as create new medicines using medical data. [ABSTRACT FROM AUTHOR] |
| Databáze: |
Complementary Index |
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