Diabetics is one of the world’s most common diseases which are caused by continued high levels of blood sugar.The risk of diabetics can be lowered if the diabetic is found at the early stage.In recent days,several ma...Diabetics is one of the world’s most common diseases which are caused by continued high levels of blood sugar.The risk of diabetics can be lowered if the diabetic is found at the early stage.In recent days,several machine learning models were developed to predict the diabetic presence at an early stage.In this paper,we propose an embedded-based machine learning model that combines the split-vote method and instance duplication to leverage an imbalanced dataset called PIMA Indian to increase the prediction of diabetics.The proposed method uses both the concept of over-sampling and under-sampling along with model weighting to increase the performance of classification.Different measures such as Accuracy,Precision,Recall,and F1-Score are used to evaluate the model.The results we obtained using K-Nearest Neighbor(kNN),Naïve Bayes(NB),Support Vector Machines(SVM),Random Forest(RF),Logistic Regression(LR),and Decision Trees(DT)were 89.32%,91.44%,95.78%,89.3%,81.76%,and 80.38%respectively.The SVM model is more efficient than other models which are 21.38%more than exiting machine learning-based works.展开更多
Purpose-Artificial Intelligence(AI)has surpassed expectations in opening up different possibilities for machines from different walks of life.Cloud service providers are pushing.Edge computing reduces latency,improvin...Purpose-Artificial Intelligence(AI)has surpassed expectations in opening up different possibilities for machines from different walks of life.Cloud service providers are pushing.Edge computing reduces latency,improving availability and saving bandwidth.Design/methodology/approach-The exponential growth in tensor processing unit(TPU)and graphics processing unit(GPU)combined with different types of sensors has enabled the pairing of medical technology with deep learning in providing the best patient care.A significant role of pushing and pulling data from the cloud,big data comes into play as velocity,veracity and volume of data with IoT assisting doctors in predicting the abnormalities and providing customized treatment based on the patient electronic health record(EHR).Findings-The primary focus of edge computing is decentralizing and bringing intelligent IoT devices to provide real-time computing at the point of presence(PoP).The impact of the PoP in healthcare gains importance as wearable devices and mobile apps are entrusted with real-time monitoring and diagnosis of patients.The impact edge computing of the PoP in healthcare gains significance as wearable devices and mobile apps are entrusted with real-time monitoring and diagnosis of patients.Originality/value-The utility value of sensors data improves through the Laplacian mechanism of preserved PII response to each query from the ODL.The scalability is at 50%with respect to the sensitivity and preservation of the PII values in the local ODL.展开更多
文摘Diabetics is one of the world’s most common diseases which are caused by continued high levels of blood sugar.The risk of diabetics can be lowered if the diabetic is found at the early stage.In recent days,several machine learning models were developed to predict the diabetic presence at an early stage.In this paper,we propose an embedded-based machine learning model that combines the split-vote method and instance duplication to leverage an imbalanced dataset called PIMA Indian to increase the prediction of diabetics.The proposed method uses both the concept of over-sampling and under-sampling along with model weighting to increase the performance of classification.Different measures such as Accuracy,Precision,Recall,and F1-Score are used to evaluate the model.The results we obtained using K-Nearest Neighbor(kNN),Naïve Bayes(NB),Support Vector Machines(SVM),Random Forest(RF),Logistic Regression(LR),and Decision Trees(DT)were 89.32%,91.44%,95.78%,89.3%,81.76%,and 80.38%respectively.The SVM model is more efficient than other models which are 21.38%more than exiting machine learning-based works.
文摘Purpose-Artificial Intelligence(AI)has surpassed expectations in opening up different possibilities for machines from different walks of life.Cloud service providers are pushing.Edge computing reduces latency,improving availability and saving bandwidth.Design/methodology/approach-The exponential growth in tensor processing unit(TPU)and graphics processing unit(GPU)combined with different types of sensors has enabled the pairing of medical technology with deep learning in providing the best patient care.A significant role of pushing and pulling data from the cloud,big data comes into play as velocity,veracity and volume of data with IoT assisting doctors in predicting the abnormalities and providing customized treatment based on the patient electronic health record(EHR).Findings-The primary focus of edge computing is decentralizing and bringing intelligent IoT devices to provide real-time computing at the point of presence(PoP).The impact of the PoP in healthcare gains importance as wearable devices and mobile apps are entrusted with real-time monitoring and diagnosis of patients.The impact edge computing of the PoP in healthcare gains significance as wearable devices and mobile apps are entrusted with real-time monitoring and diagnosis of patients.Originality/value-The utility value of sensors data improves through the Laplacian mechanism of preserved PII response to each query from the ODL.The scalability is at 50%with respect to the sensitivity and preservation of the PII values in the local ODL.
