Burden of diabetes and hypertension among people attending health camps in an urban area of Kancheepuram district

Hariharan, S.; Umadevi, R; Stephen, T.; Pradeep, S.

doi:10.18203/2394-6040.ijcmph20175771

Cited by 7 publications

(8 citation statements)

References 2 publications

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“…[14][15][16] However study by Kishore et al 14.1%, Singh et al 10.36% and Hariharan et al 10.7% obtained low prevalence than present study. [17][18][19] The prevalence of hypertension was found to be 11.1% in rural area which was comparable to study by Ghosh 10.6%, Singh et al 10.36%. 19,20 A Higher prevalence to the present study, was reported in studies by Marinayakanakoppalu et al 25.0%, Ismail et al 18.3%.…”

Section: Discussionsupporting

confidence: 74%

Prevalence of hypertension and its associated modifiable risk factors among rural and urban adults of field practice area of Jhalawar Medical College, Jhalawar

Bindal¹,

Singh²,

Kumar³

et al. 2022

Int J Community Med Public Health

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Background: The behavioral and biological risk factors, with a predisposition to the development of high blood pressure, are use of tobacco and alcohol, physical inactivity, obesity, increased fat, sodium intake, low fruit and vegetable intake. HT is called a “silent killer”. Majority of people with hypertension are unaware of the problem because it may have no warning signs or symptoms. Objectives were assessment of the prevalence of hypertension and associated modifiable risk factors among adults in the practice area of rural and urban area of Jhalawar Medical College, Jhalawar.Methods: The present observational cross-sectional study was conducted on adults of rural and urban dwellers of field practice area of Jhalawar Medical College, Jhalawar. By stratified random sampling method, 1418 participants were selected and included in our study during the study period that fulfilled our inclusion and exclusion criteria.Results: The overall prevalence of hypertension found was 18.6%. In the present study, the association of hypertension with risk factors like tobacco use, BMI, and waist hip ratio was found to be significant in rural areas. While in urban areas, the prevalence of hypertension was found to be statistically significant with calorie intake, tobacco use, and BMI and waist hip ratio. The risk factor for calorie intake was found to have an insignificant association with rural areas.Conclusions: The present study revealed that around one fifth of adults had hypertension. The prevalence of hypertension was significantly higher in urban areas than in rural areas.

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Section: Discussionsupporting

confidence: 74%

Prevalence of hypertension and its associated modifiable risk factors among rural and urban adults of field practice area of Jhalawar Medical College, Jhalawar

Bindal¹,

Singh²,

Kumar³

et al. 2022

Int J Community Med Public Health

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“…World Health Organization (WHO) records that among the total deaths 2% of deaths are due to diabetes in India [3]. Diabetes and hypertension is found to be the common non-communicable disease [4], [5]. Diabetes counts to 46.2% and hypertension counts to 4% of total deaths [6].…”

Section: Introductionmentioning

confidence: 99%

Early disease prediction algorithm for hypertension-based diseases using data aware algorithms

Shaikh

Parvati

Biradar

2022

IJEECS

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This paper <span lang="EN-US">implements a data aware early prediction of hypertension-based diseases. Automated data preprocessing method that adopts for both balanced and unbalanced data is the data aware method included in the disease classification algorithm. Proposed data aware data preprocessing method is evaluated on the ensemble learning based classification algorithm for early disease prediction. Data aware preprocessing method adopts isolation forest algorithm for outlier detection as part of the automation. Automated sampling method of applying the sampling corresponding to either balanced or unbalanced data is adopted. Performance evaluation of the proposed data aware algorithm using isolation forest algorithm for anomaly detection is experimented. Python based implementation of the proposed data aware classification algorithm inferred a better area under the curve (AUC) receiver operating characteristics (ROC) curve for isolation forest implementation in data preprocessing automation thus developed. While the individual classifiers multilayer perceptron classifier approached till 0.918 (AUC) in the ROC-AUC curve. The ensemble learning algorithm that included multilayer perceptron classifier, logistic regression classifier, support vector classifier and decision tree algorithm with the isolation forest-based anomaly detection algorithm performed better than the individual machine learning algorithm with 0.922 (AUC) in the ROC-AUC curve.</span>

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“…If unmitigated, the total number of noncommunicable disease related deaths is estimated to reach 52 million yearly by 2030 [3]. The most common noncommunicable diseases are diabetes and hypertension [4], [5], which account for nearly 46.2% and 4% of total deaths [6], respectively. Type 2 diabetes is a continuing metabolic disorder that changes blood glucose levels and is usually a consequence of the body's ineffectiveness to employ its produced insulin [7], [8].…”

Section: Introductionmentioning

confidence: 99%

Development of Disease Prediction Model Based on Ensemble Learning Approach for Diabetes and Hypertension

et al. 2019

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Early diseases prediction plays an important role for improving healthcare quality and can help individuals avoid dangerous health situations before it is too late. This paper proposes a disease prediction model (DPM) to provide an early prediction for type 2 diabetes and hypertension based on individual's risk factors data. The proposed DPM consists of isolation forest (iForest) based outlier detection method to remove outlier data, synthetic minority oversampling technique tomek link (SMOTETomek) to balance data distribution, and ensemble approach to predict the diseases. Four datasets were utilized to build the model and extract the most significant risks factors. The results showed that the proposed DPM achieved highest accuracy when compared to other models and previous studies. We also developed a mobile application to provide the practical application of the proposed DPM. The developed mobile application gathers risk factor data and send it to a remote server, so that an individual's current condition can be diagnosed with the proposed DPM. The prediction result is then sent back to the mobile application; thus, immediate and appropriate action can be taken to reduce and prevent individual's risks once unexpected health situations occur (i.e., type 2 diabetes and/or hypertension) at early stages.INDEX TERMS Diabetes, disease prediction, ensemble learning, hypertension, imbalanced data, outlier data.

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Burden of diabetes and hypertension among people attending health camps in an urban area of Kancheepuram district

Cited by 7 publications

References 2 publications

Prevalence of hypertension and its associated modifiable risk factors among rural and urban adults of field practice area of Jhalawar Medical College, Jhalawar

Prevalence of hypertension and its associated modifiable risk factors among rural and urban adults of field practice area of Jhalawar Medical College, Jhalawar

Early disease prediction algorithm for hypertension-based diseases using data aware algorithms

Development of Disease Prediction Model Based on Ensemble Learning Approach for Diabetes and Hypertension

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