Data mining is flourishing during recent years and it is establishing itself as a major discipline in Computer Science and Statistics with industrial relevance. Data mining is finding interesting structure in databases [2]. DNA is an extraordinary chip data with thousands of attributes which represents the gene expression values [4]. The DNA data set are stored in huge biological databases for several purposes [1].Cancer occurs when lumps of cells usually group together to form tumors. A growing tumor becomes a lump of cancer cells that can destroy the normal cells around the tumor and damage the body's health tissues. In the last two decades the researchers have drawn much attention about liver cancer. Liver cancer is a disease in which malignant cells form in the tissues of the liver. It is relatively rare form of cancer but has a high mortality rate. The aim of this paper is to analyze the liver cancer DNA sequence data using the generalization of Kimura Models and Markov Chain. The reasonable results verify the validity of our method.
Because of the increasing workload, people are having several clinical examinations to determine their health status, resulting in limited time. Here, we present a healthful consuming device based on rule mining that can modify your parameter dependency and recommend the varieties of meals that will boost your fitness and assist you to avoid the types of meals that increase your risk for sicknesses. Using the meals database, the data mining technique is useful for gathering meal energy from breakfast, after breakfast, lunch, after lunch, dinner, after dinner, and bedtime for ninety days. The purpose of this study is to determine to mean random plasma glucose levels and h1bc levels using the Nathan, ADAG (A1C-derived average glucose), and DTTC (Dynamic Temporal and Tactile Cueing) methods. This system can identify and recognize food images, as well as keep track of the food items ingested by the user. Deep learning techniques are mostly utilized for picture recognition and categorization. The KNN (k-nearest neighbors algorithm) classification approach is used to determine if diabetes is normal, pre-diabetic, or chronic. This study employs deep learning and a smart camera app called "calorie mom" to track nutrition from meal photographs. In addition, the commonly used measures of divisions such as accuracy, sensitivity, uniqueness, and recalling diabetic dataset using Python 3 Jupyter Notebook were employed to evaluate the performance of a machine learning classifier.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.