#Artificial academy 2 lag windows 10 2019 keygen
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When compared with the other tasks performed using the computer-aided decision system, classification is a general task, which involves allocation of a label to a query case based on the selected number of features. 13, 14 In computer-aided decision models, information technology is applied to help physicians diagnose diseases in individuals. Several diagnostic models for application in the medical field have been programmed into smart data classification methods. Recently, computational or machine intelligence in medical analyses has become greatly familiarized with medical data domains. 11 Fetal complications can occur, including fetal wastage from early pregnancy, congenital anomalies, macrosomia, shoulder dystocia, stillbirth, growth restriction, and hypoglycemia. 10 However, it involves a high risk of subsequent maternal type 2 diabetes. 9 GDM occurs in only 3–5% of all pregnancies. 7 The most common complications of type 2 diabetes mellitus are heart diseases, stroke, nephropathy, retinopathy, and neuropathy. Furthermore, cardiovascular diseases account for 70% of the deaths in type 2 diabetes mellitus. 7 Daneman 8 reported a relationship between type 1 diabetes mellitus and the progression of microvascular (retinopathy, nephropathy, and neuropathy), and most likely, macrovascular (cardiovascular, cerebrovascular, and peripheral vascular diseases), diseases into long-term complications. 6 The complications of type 1 diabetes mellitus depend on the degree and length of exposure to hyperglycemic conditions. Diabetes mellitus can be classified into three main types, namely type 1 diabetes mellitus, 4 type 2 diabetes mellitus, 5 and gestational diabetes mellitus (GDM). 3 More than 2 million deaths are attributed to cardiovascular diseases and other ailments, which form the risk factors of diabetes mellitus. 2 However, a recent study conducted in 2020 revealed that nearly 77 million people in India are affected by diabetes. 1 A survey conducted in 2011 reported that the number of senior citizens with diabetes across India in 2050 is projected to be approximately 33.3 million.
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Keywords: diabetes, GBT, feature selection, artificial flora, classificationĪccording to the World Health Organization, the number of diabetic patients rose from 108 million in 1980 to 420 million in 2014. The proposed model showed a maximum average precision of 91.64%, a recall of 97.46%, an accuracy of 99.93%, an F-score of 94.19%, and a kappa of 96.61%.Ĭonclusion: The AFA-GBT model could classify patient diagnoses into the three diabetes types efficiently.
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Results: The effectiveness of the proposed AFA-GBT model was validated using three diabetes datasets to classify patient cases into one of the three different types of diabetes. Lastly, the GBT-based classification model was applied for classifying the patients’ cases to type I, type II, or gestational diabetes mellitus. AFA was applied for selecting features, such as demographics, vital signs, laboratory tests, medications, from the patients’ electronic health records. Then, the processing occurred in two steps, namely, format conversion and data transformation. The proposed model involved preprocessing, artificial flora algorithm (AFA)-based feature selection, and gradient boosted tree (GBT)-based classification.
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Methods: Three different datasets are used to develop a novel medical data classification model. Purpose: Classification of medical data is essential to determine diabetic treatment options therefore, the objective of the study was to develop a model to classify the three diabetes type diagnoses according to multiple patient attributes. Nagaraj P, 1 Deepalakshmi P, 1 Romany F Mansour, 2 Ahmed Almazroa 3ġDepartment of Computer Science and Engineering, School of Computing, Kalasalingam Academy of Research and Education, Virudhunagar, Tamil Nadu, India 2Department of Mathematics, Faculty of Science, New Valley University, El-Kharga, Egypt 3Department of imaging Research, King Abdullah International Medical Research Center, King Saud bin Abdulaziz University for Health Science, Riyadh, Saudi Arabiaĭepartment of Computer Science and Engineering, School of Computing, Kalasalingam Academy of Research and Education, Anand Nagar, Krishnankoil, Srivilliputtur, Virudhunagar, Tamil Nadu, 626126, India