Performance Analysis of Different Classification Techniques to Design the Predictive Model for Risk Prediction and Diagnose Diabetes Mellitus at an Early Stage

2021 ◽  
pp. 177-184
Author(s):  
Asmita Ray ◽  
Debnath Bhattacharyya
Keyword(s):  
Diabetes Mellitus ◽  
Performance Analysis ◽  
Predictive Model ◽  
Risk Prediction ◽  
Early Stage ◽  
Classification Techniques

Predictive Analysis of Diabetes Mellitus Using Machine Learning Techniques

2020 ◽  
Vol 17 (8) ◽  
pp. 3449-3452
Author(s):  
M. S. Roobini ◽  
Y. Sai Satwick ◽  
A. Anil Kumar Reddy ◽  
M. Lakshmi ◽  
D. Deepa ◽  
...  
Keyword(s):  
Diabetes Mellitus ◽  
Machine Learning ◽  
Early Stage ◽  
The Body ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Pima Indians ◽  
Classification Techniques ◽  
Learning Techniques ◽  
Prediction Of Diabetes

In today’s world diabetes is the major health challenges in India. It is a group of a syndrome that results in too much sugar in the blood. It is a protracted condition that affects the way the body mechanizes the blood sugar. Prevention and prediction of diabetes mellitus is increasingly gaining interest in medical sciences. The aim is how to predict at an early stage of diabetes using different machine learning techniques. In this paper basically, we use well-known classification that are Decision tree, K-Nearest Neighbors, Support Vector Machine, and Random forest. These classification techniques used with Pima Indians diabetes dataset. Therefore, we predict diabetes at different stage and analyze the performance of different classification techniques. We Also proposed a conceptual model for the prediction of diabetes mellitus using different machine learning techniques. In this paper we also compare the accuracy of the different machine learning techniques to finding the diabetes mellitus at early stage.

scholarly journals Diabetes Mellitus Prediction using Classification Techniques

2020 ◽  
Vol 9 (5) ◽  
pp. 2080-2084 ◽  
Keyword(s):  
Diabetes Mellitus ◽  
Decision Tree ◽  
Early Stage ◽  
Premature Death ◽  
Support Vector ◽  
Classification Methods ◽  
Classification Techniques ◽  
Vector Machines ◽  
Patients With Diabetes ◽  
The Given

Diabetes is a metabolic disease affecting people in almost every country and it may lead to severe problems like stroke, kidney failure or premature death if it is not predicted at the early stage. To mitigate this many researchers are working to predict the diabetes at early stage using several methods. Different accessible conventional techniques are carried out to diagnose diabetes depend on physical and substance tests. Several data mining methods were designed to overcome these uncertainties. Classification techniques like Decision Tree, KNearest Neighbors, and Support Vector Machines are used to classify the patients with diabetes mellitus. The performance of these applied techniques are determined using the factors precision, accuracy, Sensitivity, and Specificity. The results obtained proved that SVM outperforms decision tree and KNN with highest accuracy of 90.23%. Performance analysis of these classification methods helps us to decide which appropriate technique to choose in future for analysing the given dataset.

908-P: Predictive Model for Glycemic Level of People with Diabetes Mellitus Using Data Extracted from Electronic Medical Records in Primary Care

Diabetes ◽  
2020 ◽  
Vol 69 (Supplement 1) ◽  
pp. 908-P
Author(s):  
SOSTENES MISTRO ◽  
THALITA V.O. AGUIAR ◽  
VANESSA V. CERQUEIRA ◽  
KELLE O. SILVA ◽  
JOSÉ A. LOUZADO ◽  
...  
Keyword(s):  
Diabetes Mellitus ◽  
Primary Care ◽  
Predictive Model ◽  
Electronic Medical Records ◽  
Medical Records ◽  
Glycemic Level ◽  
Using Data

scholarly journals Optimal multi-layer perceptron parameters for early stage diabetes risk prediction

2021 ◽  
Vol 1073 (1) ◽  
pp. 012070
Author(s):  
V N Wijayaningrum ◽  
T H Saragih ◽  
N N Putriwijaya
Keyword(s):  
Risk Prediction ◽  
Early Stage ◽  
Diabetes Risk ◽  
Multi Layer Perceptron

scholarly journals Radiomics is feasible for prediction of spread through air spaces in patients with nonsmall cell lung cancer

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Yuki Onozato ◽  
Takahiro Nakajima ◽  
Hajime Yokota ◽  
Jyunichi Morimoto ◽  
Akira Nishiyama ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Predictive Model ◽  
Cell Lung Cancer ◽  
Early Stage ◽  
Characteristic Curve ◽  
Sublobar Resection ◽  
Tumor Spread ◽  
Early Stage Disease ◽  
Significant Difference ◽  
Spread Through Air Spaces

AbstractTumor spread through air spaces (STAS) in non-small-cell lung cancer (NSCLC) is known to influence a poor patient outcome, even in patients presenting with early-stage disease. However, the pre-operative diagnosis of STAS remains challenging. With the progress of radiomics-based analyses several attempts have been made to predict STAS based on radiological findings. In the present study, patients with NSCLC which is located peripherally and tumors ≤ 2 cm in size on computed tomography (CT) that were potential candidates for sublobar resection were enrolled in this study. The radiologic features of the targeted tumors on thin-section CT were extracted using the PyRadiomics v3.0 software package, and a predictive model for STAS was built using the t-test and XGBoost. Thirty-five out of 226 patients had a STAS histology. The predictive model of STAS indicated an area under the receiver-operator characteristic curve (AUC) of 0.77. There was no significant difference in the overall survival (OS) for lobectomy between the predicted-STAS (+) and (−) groups (p = 0.19), but an unfavorable OS for sublobar resection was indicated in the predicted-STAS (+) group (p < 0.01). These results suggest that radiomics with machine-learning helped to develop a favorable model of STAS (+) NSCLC, which might be useful for the proper selection of candidates who should undergo sublobar resection.

Assessment of the therapeutic potential of hesperidin and proteomic resolution of diabetes-mediated neuronal fluctuations expediting Alzheimer’s disease

RSC Advances ◽  
2015 ◽  
Vol 5 (58) ◽  
pp. 46965-46980 ◽  
Author(s):  
Sapna Khowal ◽  
Malik M. A. Mustufa ◽  
Naveen K. Chaudhary ◽  
Samar Husain Naqvi ◽  
Suhel Parvez ◽  
...  
Keyword(s):  
Diabetes Mellitus ◽  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Therapeutic Potential ◽  
Neuronal Damage ◽  
Early Stage ◽  
Type Iii

Alzheimer’s disease (AD) has been proposed as type III diabetes mellitus. Prognosis and early stage diagnosis of AD is essentially required in diabetes to avoid extensive irreversible neuronal damage.

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