A study to classify Non-Dipper/Dipper blood pressure pattern of type 2 diabetes mellitus patients without Holter device

Altikardes, Zehra Aysun; Erdal, Hasan; Baba, Ahmet Fevzi; Tezcan, Hakan; Fak, Ali Serdar; Korkmaz, Hayriye

doi:10.1109/wccais.2014.6916555

Cited by 5 publications

(2 citation statements)

References 6 publications

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“…Brain et al [20] experimented by Artificial Neural Network, Multilayer Perceptron to conclude that HIV status of a person based could be predicted based on demographic data. Altikardes et al [21] studied many classifiers like Decision trees, naive Bayes, support vector machines, voted perceptron, multi-layer perceptron, logistic regression etc. for classification of Non-Dipper or Dipper Blood Pressure Pattern without Holter Device for the patients suffering with Type 2 Diabetes Mellitus.…”

Section: Literature Reviewmentioning

confidence: 99%

A methodology for customizing clinical tests for esophageal cancer based on patient preferences

Roy

Bhattacharya

Guin

2019

Artificial Intelligence in Medicine

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Section: Literature Reviewmentioning

confidence: 99%

A methodology for customizing clinical tests for esophageal cancer based on patient preferences

Roy

Bhattacharya

Guin

2019

Artificial Intelligence in Medicine

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“…Altikardes Z.A et al [4] designs an expert system to predict the Non-Dipping/Dipping Patterns by using data collected from various sources. Decision Tree and Naïve-Bayes classifiers are used.…”

Section: Related Workmentioning

confidence: 99%

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

Sowjanya

Singhal

Choudhary

2015

2015 IEEE International Advance Computing Conference (IACC)

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Diabetes mellitus (DM) is reaching possibly epidemic proportions in India. The degree of disease and destruction due to diabetes and its potential complications are enormous, and originated a significant health care burden on both households and society. The concerning factor is that diabetes is now being proven to be linked with a number of complications and to be occurring at a comparatively younger age in the country. In India, the migration of people from rural to urban areas and corresponding modification in lifestyle are all moving the degree of diabetes. Deficiency of knowledge about diabetes causes untimely death among the population at large. Therefore, acquiring a proficiency that should spread awareness about diabetes may affect the people in India. In this work, a mobile/android application based solution to overcome the deficiency of awareness about diabetes has been shown. The application uses novel machine learning techniques to predict diabetes levels for the users. At the same time, the system also provides knowledge about diabetes and some suggestions on the disease. A comparative analysis of four machine learning (ML) algorithms were performed. The Decision Tree (DT) classifier outperforms amongst the 4 ML algorithms. Hence, DT classifier is used to design the machinery for the mobile application for diabetes prediction using real world dataset collected from a reputed hospital in the Chhattisgarh state of India.

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Prediction of esophageal cancer using demographic, lifestyle, patient history, and basic clinical tests

Roy

Bhattacharya

Guin

2017

Int J Adv Eng Sci Appl Math

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A study to classify Non-Dipper/Dipper blood pressure pattern of type 2 diabetes mellitus patients without Holter device

Cited by 5 publications

References 6 publications

A methodology for customizing clinical tests for esophageal cancer based on patient preferences

A methodology for customizing clinical tests for esophageal cancer based on patient preferences

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

Prediction of esophageal cancer using demographic, lifestyle, patient history, and basic clinical tests

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