Early Risk Prediction of Diabetes Based on GA-Stacking

Early risk prediction of diabetes could help doctors and patients to pay attention to the disease and intervene as soon as possible, which can effectively reduce the risk of complications. In this paper, a GA-stacking ensemble learning model is proposed to improve the accuracy of diabetes risk prediction. Firstly, genetic algorithms (GA) based on Decision Tree (DT) is used to select individuals with high adaptability, that is, a subset of attributes suitable for diabetes risk prediction. Secondly, the optimized convolutional neural network (CNN) and support vector machine (SVM) are used as the primary learners of stacking to learn attribute subsets, respectively. Then, the output of CNN and SVM is used as the input of the mate learner, the fully connected layer, for classification. Qingdao desensitization physical examination data from 1 January 2017 to 31 December 2019 is used, which includes body temperature, BMI, waist circumference, and other indicators that may be related to early diabetes. We compared the performance of GA-stacking with K-nearest neighbor (KNN), SVM, logistic regression (LR), Naive Bayes (NB), and CNN before and after adding GA through the average prediction time, accuracy, precision, sensitivity, specificity, and F1-score. Results show that prediction efficiency can be improved by adding GA. GA-stacking has higher prediction accuracy. Moreover, the strong generalization ability and high prediction efficiency of GA-stacking have also been verified on the early-stage diabetes risk prediction dataset published by UCI.

PREDICTION OF DIABETES SCREENING BY USING DATA MINING ALGORITHMS

Diabetes is one of the most common non-communicable diseases in the world. Diabetes affects the ability to produce the hormone insulin. Thus, complications may occur if diabetes remains untreated and unidentified. That features a significant contribution to increased morbidity, mortality, and admission rates of patients in both developed and developing countries. When disease is not detected early, it leads to complications. Medical records of the cases were retrospective. Anthropometric and biochemical information was collected. From this data, four ML classification algorithms, including Decision Tree (J48), Naive-Bayes, PART rule induction, and JRIP, were used to prognosticate diabetes. Precision, recall, F-Measure, Receiver Operating Characteristics (ROC) scores, and the confusion matrix were calculated to determine the performance of the various algorithms. The performance was also measured by sensitivity and specificity. They have high classification accuracy and are generally comparable in predicting diabetes and free diabetes patients. Among the selected algorithms tested, the Decision Tree Classifier (J48) algorithm scored the highest accuracy and was the best predictor, with a classification accuracy of 92.74%.

Prediction of Diabetes with Deep Neural Network

Diabetic mellitus is hitting the globe since decades. it often leads to dangerous health issues such as kidney problems, heart strokes, nervous system disturbance and eye problems etc. The prediction and detection of such a deadly disease is pivotal. In the conducted study, we have built a diabetic prediction model which is based on deep neural networks. We performed our experiments with two-fold and four-fold cross validation. Our diabetic prediction model has reported an accuracy of 98.45% which is quite high.

Evaluation of Machine Learning Methods Developed for Prediction of Diabetes Complications: A Systematic Review

Background: With the rising prevalence of diabetes, machine learning (ML) models have been increasingly used for prediction of diabetes and its complications, due to their ability to handle large complex data sets. This study aims to evaluate the quality and performance of ML models developed to predict microvascular and macrovascular diabetes complications in an adult Type 2 diabetes population. Methods: A systematic review was conducted in MEDLINE®, Embase®, the Cochrane® Library, Web of Science®, and DBLP Computer Science Bibliography databases according to the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) checklist. Studies that developed or validated ML prediction models for microvascular or macrovascular complications in people with Type 2 diabetes were included. Prediction performance was evaluated using area under the receiver operating characteristic curve (AUC). An AUC >0.75 indicates clearly useful discrimination performance, while a positive mean relative AUC difference indicates better comparative model performance. Results: Of 13 606 articles screened, 32 studies comprising 87 ML models were included. Neural networks (n = 15) were the most frequently utilized. Age, duration of diabetes, and body mass index were common predictors in ML models. Across predicted outcomes, 36% of the models demonstrated clearly useful discrimination. Most ML models reported positive mean relative AUC compared with non-ML methods, with random forest showing the best overall performance for microvascular and macrovascular outcomes. Majority (n = 31) of studies had high risk of bias. Conclusions: Random forest was found to have the overall best prediction performance. Current ML prediction models remain largely exploratory, and external validation studies are required before their clinical implementation. Protocol Registration: Open Science Framework (registration number: 10.17605/OSF.IO/UP49X).

Prediction of Alzheimer Disease Using Pearson Recursive Graph Convolutional Neural Network

Alzheimer diseases are very hard to identify at beginning stage and also medication is available. So, the only way to protect those people is to predict the Alzheimer disease before it reaches the peak. More studies in diabetes show that there is a link between Diabetes and Alzheimer. Initially the prediction of diabetes is done using most relevant parameters, which detects the Diabetes. Then the severity level of diabetes is identified using some scoring levels. Based on scoring levels of diabetes, it is classified in to Type1 and Type2 using Machine Learning algorithms. When Diabetes reaches a worst case, it may affect any organs in the human body, whereas Type 2 Diabetes has associated with rare diseases which literally affects the brain and leads to cognitive impairment. After predicting the patients having cognitive impairment by applying classification algorithms are further examined to check whether it leads to Alzheimer disease. For this prediction the most relevant parameters which are common to Diabetes and Alzheimer is identified. Further identified parameters are used for prediction of Alzheimer disease with high accuracy which is helpful for taking precaution measures. In this proposed work, the most relevant features are selected using Pearson correlation-based feature elimination method and the diagnosis of the diabetes are carried using the Graph convolutional neural network (GCN). The measures of performance of the proposed work are calculated with various factors like Sensitivity measure, Recall, Precision, F-Measure. Proposed has achieved highest of 98.91%, 97.01%, 98.62%, 98.91% in above metrics.