High blood pressure prediction based on AAA using J48 classifier

Nimmala, Satyanarayana; Ramadevi, Y.; Chari, K. Koteswara

doi:10.1109/spaces.2018.8316330

Cited by 12 publications

(10 citation statements)

References 10 publications

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“…Zhang et al [17] attempted to predict hypertension by using medical indicators based on the C4.5 DT, with an accuracy of 81.58%. Satyanarayana et al [18] tried to predict hypertension by using age, anger, and anxiety based on the J48 classifier with an accuracy of 84.25%. The method proposed in this Letter obtained an accuracy of 84.9% and an F1_score of 0.841.…”

Section: Resultsmentioning

confidence: 99%

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Exploring the relationship between hypertension and nutritional ingredients intake with machine learning

Liu

Jiang

et al. 2020

Healthcare Technology Letters

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Section: Resultsmentioning

confidence: 99%

“…Zhang et al [17] proposed an improved C4.5 decision tree (DT) algorithm to predict hypertension using medical data from hospital patients, with a final accuracy of 81.58%. Satyanarayana et al [18] predicted whether a person is prone to hypertension through age, anger, and anxiety using a J48 classifier with a final accuracy of 84.25%.…”

Section: Related Workmentioning

confidence: 99%

Exploring the relationship between hypertension and nutritional ingredients intake with machine learning

Liu

Jiang

et al. 2020

Healthcare Technology Letters

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“…J48 algorithm is basically a development of other Decision Tree algorithms such as C4.5 and ID3 [23]. J48 is used for a single or multi-class classification model with a decision tree model.…”

Section: J48 Algorithmmentioning

confidence: 99%

A Cardiotocographic Classification using Feature Selection: A comparative Study

Prasetyo

Prastyo²,

Arti³

2021

j.inf.tech.comp.eng

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Cardiotocography is a series of inspections to determine the health of the fetus in pregnancy. The inspection process is carried out by recording the baby's heart rate information whether in a healthy condition or contrarily. In addition, uterine contractions are also used to determine the health condition of the fetus. Fetal health is classified into 3 conditions namely normal, suspect, and pathological. This paper was performed to compare a classification algorithm for diagnosing the result of the cardiotocographic inspection. An experimental scheme is performed using feature selection and not using it. CFS Subset Evaluation, Info Gain, and Chi-Square are used to select the best feature which correlated to each other. The data set was obtained from the UCI Machine Learning repository available freely. To find out the performance of the classification algorithm, this study uses an evaluation matrix of precision, Recall, F-Measure, MCC, ROC, PRC, and Accuracy. The results showed that all algorithms can provide fairly good classification. However, the combination of the Random Forest algorithm and the Info Gain Feature Selection gives the best results with an accuracy of 93.74%.

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“…Mental Health disorders also demonstrate co morbidities with other health ailments. N. Satyanarayana et al [42] ruled out that Age, Anger and Anxiety has temporary effects on Blood Pressure Raise in patients. Other factors like obesity also might have effects on BP increase effect, the study of future scope as per the authors quoting.…”

Section: Mental Health Analysismentioning

confidence: 99%

Intelligent health risk prediction systems using machine learning: a review

Shinde

Rajeswari

2018

IJET

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Humans are considered to be the most intelligent species on the mother earth and are inherently more health conscious. Since Centuries mankind has discovered various proven healthcare systems. To automate the process and predict diseases more accurately machine learning methods are gaining popularity in research community. Machine Learning methods facilitate development of the intelligence into a machine, so that it can perform better in the future using the learned experience. Machine learning methods application on electronic health record dataset could provide valuable information and predication of health risks.The aim of this research review paper are four-fold: i) serve as a guideline for researchers who are new to machine learning area and want to contribute to it, ii) provide state-of-the-art survey of machine learning, iii) application of machine learning techniques in the health prediction, and iv) provides further research directions required into health prediction system using machine learning.

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High blood pressure prediction based on AAA using J48 classifier

Cited by 12 publications

References 10 publications

Exploring the relationship between hypertension and nutritional ingredients intake with machine learning

Exploring the relationship between hypertension and nutritional ingredients intake with machine learning

A Cardiotocographic Classification using Feature Selection: A comparative Study

Intelligent health risk prediction systems using machine learning: a review

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