An automatic heart disease prediction using cluster-based bi-directional LSTM (C-BiLSTM) algorithm

Dileep, P.; Rao, K. Nageswara; Bodapati, Prajna; Gokuruboyina, Sitaratnam; Peddi, Revathy; Grover, Amit; Sheetal, Anu

doi:10.1007/s00521-022-07064-0

Cited by 46 publications

(12 citation statements)

References 19 publications

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“…Considering the high correlation between pulse and BP, BiLSTM 16 is applied to learn the relation between pulse and BP, and further estimate the BP waveform. The BiLSTM structure consists of two one‐way LSTMs superimposed up and down, one transmitting data forward and the other transmitting data backward.…”

Section: Methodsmentioning

confidence: 99%

Non‐invasive measurement of blood pressure waveform based on spatiotemporal multi‐dimensional pulse waves

Lin,

Wang,

Zhang

et al. 2023

Int J Imaging Syst Tech

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Continuous monitoring of blood pressure (BP) waveform is essential for the prevention and diagnosis of cardiovascular diseases because it contains rich information. A vision‐based non‐invasive measurement method of BP waveform was proposed in this work. A binocular vision pulse acquisition system was utilized to collect continuous pulse images in a predetermined area of radial artery, and multi‐dimensional pulse waves were extracted from image sequences. By using Bi‐directional Long‐Short Term Memory (BiLSTM) networks, a model suitable for the inputs of multitype pulse waves under multi‐length windows has been established. The model can measure BP wave with high accuracy more than 0.94 (correlation coefficient). Measurement errors are less than (4.65 ± 5.80) mmHg for systolic blood pressure and (6.45 ± 7.40) mmHg for diastolic blood pressure. This study breaks through the limitation of obtaining BP from single‐dimensional pulse wave in previous studies and provides a new idea for non‐invasive measurement of human BP waveform.

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Section: Methodsmentioning

confidence: 99%

Non‐invasive measurement of blood pressure waveform based on spatiotemporal multi‐dimensional pulse waves

Lin,

Wang,

Zhang

et al. 2023

Int J Imaging Syst Tech

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“…The real-time dataset and UCI heart disease dataset are utilized to compare DL approaches to traditional approaches. Dileep et al [27] developed C-BiLSTM to increase the accuracy of existing approaches. The real-time and UCI heart disease datasets are utilized for performance findings, and both datasets are passed through the K-Means clustering method to remove duplicate information, and the HD predicted by the C-BiLSTM method.…”

Section: Learning-based Heart Disease Predictionmentioning

confidence: 99%

A Review: Machine Learning and Data Mining Approaches for Cardiovascular Disease Diagnosis and Prediction

Srinivasa Rao,

Muneeswari

2024

EAI Endorsed Trans Perv Health Tech

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INTRODUCTION: Cardiovascular disease (CVD) is the most common cause of death worldwide, and its prevalence is rising in low-resource settings and among those with lower incomes. OBJECTIVES: Machine learning (ML) algorithms are quickly evolving and being implemented in medical procedures for CVD diagnosis and treatment decisions. Every day, the healthcare business creates massive amounts of data. However, the majority of it is inadequately utilized. Efficient techniques for extracting knowledge from these datasets for clinical diagnosis or other uses are scarce. METHODS: ML is being applied in the healthcare industry all over the world. In the health dataset, ML approaches useful in the prevention of locomotor disorders and heart disease. RESULTS: The revelation of such vital information allows researchers to acquire significant insight into how to use the proper treatment and diagnosis for a specific patient. Researchers study enormous volumes of complex healthcare data using various ML approaches, which improves healthcare professionals in disease prediction. CONCLUSION: The goal of this study is to summarize some of the current research on predicting heart diseases utilizing machine learning and data mining techniques, analyze the various mining algorithm combinations employed, and determine which techniques are useful and efficient. Future directions in prediction systems have also been considered.

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“…( 5) Opensourcing reproducible code for all the experiments in the supplementary material. Contemporary arts exist, such as [19] and [23]. Nevertheless, they do not train on the entire dataset and do not perform feature selection.…”

Section: Contributionsmentioning

confidence: 99%

Feature-Limited Prediction on the UCI Heart Disease Dataset

Alfadli¹,

Almagrabi²

2023

Computers, Materials &Amp; Continua

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Heart diseases are the undisputed leading causes of death globally. Unfortunately, the conventional approach of relying solely on the patient's medical history is not enough to reliably diagnose heart issues. Several potentially indicative factors exist, such as abnormal pulse rate, high blood pressure, diabetes, high cholesterol, etc. Manually analyzing these health signals' interactions is challenging and requires years of medical training and experience. Therefore, this work aims to harness machine learning techniques that have proved helpful for data-driven applications in the rise of the artificial intelligence era. More specifically, this paper builds a hybrid model as a tool for data mining algorithms like feature selection. The goal is to determine the most critical factors that play a role in discriminating patients with heart illnesses from healthy individuals. The contribution in this field is to provide the patients with accurate and timely tentative results to help prevent further complications and heart attacks using minimum information. The developed model achieves 84.24% accuracy, 89.22% Recall, and 83.49% Precision using only a subset of the features.

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An automatic heart disease prediction using cluster-based bi-directional LSTM (C-BiLSTM) algorithm

Cited by 46 publications

References 19 publications

Non‐invasive measurement of blood pressure waveform based on spatiotemporal multi‐dimensional pulse waves

Non‐invasive measurement of blood pressure waveform based on spatiotemporal multi‐dimensional pulse waves

A Review: Machine Learning and Data Mining Approaches for Cardiovascular Disease Diagnosis and Prediction

Feature-Limited Prediction on the UCI Heart Disease Dataset

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