RETRACTED ARTICLE: Optimal prediction of attacks and arterial stiffness effects on heart disease by hybrid machine learning algorithm

Elumalai, Anupriya; Maruthi, Puvvadi Baby; Gautam, Neha; Priyadharshini, S.; Suganthy, M.

doi:10.1007/s12652-020-02706-4

Cited by 6 publications

(5 citation statements)

References 28 publications

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“…The main idea behind lemmatization is to prevent sparse data during training, as different inflected forms of the same lemma may occur infrequently (or not at all) 20 – 22 , 28 – 31 This may result in neglecting important parts of syntax. The abstract analyzes the morphological structure of the words.…”

Section: Methodsmentioning

confidence: 99%

“…An origin text mining technique is suggested to pull details from medical devices. 22 Nevertheless, several issues exist in most of the earlier investigations.…”

Section: Introductionmentioning

confidence: 99%

“…This technique uses text mining approaches to pull attributes and then uses a deep learning prototype to diagnose casualty moment. An origin text mining technique is suggested to pull details from medical devices 22 …”

Section: Introductionmentioning

confidence: 99%

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Automated detection of coronary artery disease comparing arterial fat accumulation using CNN

Sinha¹,

Sharma

2022

J. Electron. Imag.

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. Congenital heart failure (CHF) due to congestion in the blood is a serious cardiac problem correlated with crippling symptoms and leading to a rising death rate, monumental health care spending, and reduced quality of life. Heart disease prevention is among the most crucial functions of any medical system, as many people are prone to heart attacks worldwide. Although several segmentation methods for great vessels and the heart have been proposed in the research, they are not successful when applied to the health records of congenital heart disease. In this proposed work, the thickness and fat accumulation of most arteries are measured and analyzed, and then the measurement is synthesized with the corresponding width of the blood vessels of arteries; this data is used for training purposes in the convolutional neural network with one-off cross-validation and regularization. Using the CNN model, a confusion matrix is created and different statistical parameters such as accuracy sensitivity, specificity, precision, and f-score are generated. The final average accuracy was 97%, precision was 98.13%, and F-score was 98.36%. The results indicate that the CNN-based strategy can distinguish healthy hearts from those with prior cardiovascular disease.

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Section: Methodsmentioning

confidence: 99%

“…An origin text mining technique is suggested to pull details from medical devices. 22 Nevertheless, several issues exist in most of the earlier investigations.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Automated detection of coronary artery disease comparing arterial fat accumulation using CNN

Sinha¹,

Sharma

2022

J. Electron. Imag.

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“…Due to the residents' modern lifestyle, there is a risk of mortality and morbidity from diseases like heart disease, chronic respiratory disease, and diabetes [4]. Nowadays, millions of people worldwide suffer and die from many diseases [5]. The majority of people with multiple disorders are also distressed by numerous infections.…”

Section: Introductionmentioning

confidence: 99%

Machine learning approach for predicting heart and diabetes diseases using data-driven analysis

Sekar

Selvarajan

2023

IJ-AI

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Environmental changes and food habits affect people's health with numerous diseases in today's life. Machine learning is a technique that plays a vital role in predicting diseases from collected data. The health sector has plenty of electronic medical data, which helps this technique to diagnose various diseases quickly and accurately. There has been an improvement in accuracy in medical data analysis as data continues to grow in the medical field. Doctors may have a hard time predicting symptoms accurately. This proposed work utilized Kaggle data to predict and diagnose heart and diabetic diseases. The diseases heart and diabetes are the foremost cause of higher death rates for people. The dataset contains target features for the diagnosis of heart disease. This work finds the target variable for diabetic disease by comparing the patient's blood sugars to normal levels. Blood pressure, body mass index (BMI), and other factors diagnose these diseases and disorders. This work justifies the filter method and principal component analysis for selecting and extracting the feature. The main aim of this work is to highlight the implementation of three ensemble techniques-Adaptive boost, Extreme Gradient boosting, and Gradient boosting-as well as the emphasis placed on the accuracy of the results.

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“…Some other abnormalities are venous thromboembolism which is the clotting of blood in the vein, and aortic aneurysms which allows the artery to widen suddenly which is already weakened enough to do so [21]. So from the above all the statistics we can safely conclude that it is very difficult to diagnose heart disease just from the visual perspective because it has many risk factors and usually the heart diseases or heart attack doesn't come with prominent noticeable symptoms [22][23][24][25]. Some important signs of the patient like triglyceride, cholesterol, diabetes, heartbeat, blood pressure and then measurement from ECG EEG EMG are very important and represent a important role in diagnosing and detecting the stages of heart and whether in future the patient will suffer a cardiac arrest or not.…”

Section: Introductionmentioning

confidence: 99%

A hybrid machine learning approach for heart disease prediction using hyper parameter optimization

Sinha¹,

Sharma

2022

Preprint

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Heart disease is a serious terminal condition in most parts of the world. The acute lack of medical professionals, expertise, and technology to identify important signs. So a smart and efficient model and technology is required to lead early diagnosis of heart disease. The current study proposes a new experience-based method namely HSPUCD (Heart stage prediction using clinical data) to forecast cardiac disorders utilizing hybrid machine learning. Type 4 protocols Cross Validation is used to assess a model's competence on unknown data. Hyper parameters were modified to regulate the suggested model's behavior and performance. The suggested system is based on extensive research. Data pre-processing was utilized to discover the most relevant network traffic and generate the initial candidate collection of feature characteristics. The suggested model additionally uses automated feature selection to extract features from a candidate set of features and selects the most relevant subset of features from this candidate set. Crow search algorithm is used to extract data characteristics from the pre-processed data. Finally, an online training classifier is utilized to categorize the data and forecast the result. The proposed strategy greatly increases the system's illness prediction accuracy with less noise and more advanced regression approaches using existing medical datasets. Mathematical results depicts the effectiveness of the proposed approach in comparison with others.

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RETRACTED ARTICLE: Optimal prediction of attacks and arterial stiffness effects on heart disease by hybrid machine learning algorithm

Cited by 6 publications

References 28 publications

Automated detection of coronary artery disease comparing arterial fat accumulation using CNN

Automated detection of coronary artery disease comparing arterial fat accumulation using CNN

Machine learning approach for predicting heart and diabetes diseases using data-driven analysis

A hybrid machine learning approach for heart disease prediction using hyper parameter optimization

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