MobDBTest: A machine learning based system for predicting diabetes risk using mobile devices

Sowjanya, K; Singhal, Ayush; Choudhary, Chaitali

doi:10.1109/iadcc.2015.7154738

Cited by 51 publications

(15 citation statements)

References 10 publications

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“…The SVM, Decision Tree, and k-NN classification algorithms were chosen because they are commonly used for medical mining as presented in the Literature Review section of this paper. In addition, they outperform other applied algorithms, as shown in [6,12,13]. Furthermore, SVM was used in this study because it was not applied to MERS-CoV dataset in recent studies [6,7].…”

Section: Data Miningmentioning

confidence: 92%

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Selecting Accurate Classifier Models for a MERS-CoV Dataset

Almoammar¹,

Alhenaki²,

Kurdi³

2018

Advances in Intelligent Systems and Computing

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The Middle East Respiratory Syndrome Coronavirus (MERS-CoV) is a viral respiratory disease that is spreading worldwide necessitating to have an accurate diagnosis system that accurately predicts infections. As data mining classifiers can greatly assist in enhancing the prediction accuracy of diseases in general. In this paper, classifier model performance for two classification types:(1) binary and (2) multi-class were tested on a MERS-CoV dataset that consists of all reported cases in Saudi Arabia between 2013 and 2017. A cross-validation model was applied to measure the accuracy of the Support Vector Machine (SVM), Decision Tree, and k-Nearest Neighbor (k-NN) classifiers. Experimental results demonstrate that SVM and Decision Tree classifiers achieved the highest accuracy of 86.44% for binary classification based on healthcare personnel class. On the other hand, for multiclass classification based on city class, the decision tree classifier had the highest accuracy among the remaining classifiers; although it did not reach a satisfactory accuracy level (42.80%). This work is intended to be a part of a MERS-CoV prediction system to enhance the diagnosis of MERS-CoV disease.

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Section: Data Miningmentioning

confidence: 92%

“…The authors in [13] introduce a MobDBTest Android mobile application. MobDBTest uses machine learning techniques to predict diabetes levels for the users.…”

Section: Literature Reviewmentioning

confidence: 99%

Selecting Accurate Classifier Models for a MERS-CoV Dataset

Almoammar¹,

Alhenaki²,

Kurdi³

2018

Advances in Intelligent Systems and Computing

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show abstract

“…The final output achieved the greater accuracy of prediction which is 3.04% when compared to other outputs.This model proves the sufficiency of the data set quality. Sowjanya et al [13] proposed a mobile/android application based solution to eradicate the insufficiency of consciousness about diabetes. The application utilizes machine learning strategies to find out diabetes levels for the users.…”

Section: Literature Reviewmentioning

confidence: 99%

Correlation Feature Selection (CFS) and Probabilistic Neural Network (PNN) for Diabetes Disease Prediction

Kalaiselvi¹,

Sujarani²

2018

IJET

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The healthcare sector is a broad area with the abundance of patient information, which creates enormously large records day b y day. Though the scientific industry is rich in information but it is poor in knowledge. Diabetics are considered as a primary health issue of the world. As per the WHO 2014 survey According to WHO 2014 report, over 422 million people are affected from the diabetics globa lly. In the minimization of massive investigations implied on the patients, the data mining uses many mechanisms and strategies to diagnose the diabetic problem. The main objective of this proposal is to introduce assemble Data Mining based Diabetes Disease Prediction System which provides a detailed analysis of diabetics using the database of diabetics patient. The formulated work comprises of two stages such as feature selection ad prediction methods which are made known to maximize the outputs of diabetes disease predi ction. Initially Correlation Feature Selection (CFS) is formulated to identify the salient features for the diabetic repository. The identified features are fed into the classifier named Probabilistic Neural Network (PNN) classifier. As the diabetic of the patient is classified using PNN meanwhile the accuracy can be finetuned when using the identified features. Depending on the category of data, the diabetic information is gathered from the learning repository. The outputs are correlated with the current algorithms namely Back Propagation Neural Network (BPNN), Multilayer Perceptron, Neural Network (MLPNN) were used to fetch the outputs.

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“…The speed of the server is high in server edition, whereas in the desktop edition it is easier to use with graphical user interface, instead in server edition the command prompt is used for the configuration. [9] …”

Section: Ubuntumentioning

confidence: 99%

Data analysis for chronic disease -diabetes using map reduce technique

Ramkumar¹,

Prakash²,

Sangeetha³

2016

2016 International Conference on Computer Communication and Informatics (ICCCI)

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Chronic disease endured for a long period of time. They are only be controlled but cannot be cured completely. Most of the people in the world are affected by chronic disease. In foreign countries like U.S. most of the death happens due to chronic disease. Some of the chronic diseases are Allergy, Cancer, Asthma, Heart disease, Glaucoma, Obesity, viral diseases such as Hepatitis C and HIV/AIDS. Of all the diseases Diabetes is the most hazardous disease. Diabetes means that blood glucose (blood sugar is too high. It is categorized into two divisions: Diabetes of category 1 and diabetes of category 2. In category 1, the human body does not make insulin, people with type1 need to take insulin every day. In type 2 the glucose level is of very high in the blood, it is one of the most common forms of diabetes. In type 2 diabetes, need to do physical activity and should have proper diet.[8] The analysis on the data is performed using Big data analytics framework Hadoop. Hadoop framework is used to process large data sets. The analysis is done using map reduce algorithm.

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MobDBTest: A machine learning based system for predicting diabetes risk using mobile devices

Cited by 51 publications

References 10 publications

Selecting Accurate Classifier Models for a MERS-CoV Dataset

Selecting Accurate Classifier Models for a MERS-CoV Dataset

Correlation Feature Selection (CFS) and Probabilistic Neural Network (PNN) for Diabetes Disease Prediction

Data analysis for chronic disease -diabetes using map reduce technique

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