Efficient Prediction of Cardiovascular Disease Using Machine Learning Algorithms With Relief and LASSO Feature Selection Techniques

Ghosh, Pronab; Azam, Sami; Jonkman, Mirjam; Karim, Asif; Shamrat, F. M. Javed Mehedi; Ignatious, Eva; Shultana, Shahana; Beeravolu, Abhijith Reddy; Boer, Friso De

doi:10.1109/access.2021.3053759

Cited by 346 publications

(146 citation statements)

References 69 publications

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“…This data needs preprocessing as it contains special characters, hyperlinks, retweets, emoji, and stickers. Natural language processing is used to preprocess the data and to make it suitable to implement a supervised classification algorithm [9]. After removing the special characters, data is tokenized.…”

Section: Methodsmentioning

confidence: 99%

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Sentiment analysis on twitter tweets about COVID-19 vaccines usi ng NLP and supervised KNN classification algorithm

Shamrat

Chakraborty

Imran³

et al. 2021

IJEECS

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The pandemic has taken the world by storm. Almost the entire world went into lockdown to save the people from the deadly COVID-19. Scientists around the around have come up with several vaccines for the virus. Amongthem, Pfizer, Moderna, and AstraZeneca have become quite famous. General people however have been expressing their feelings about the safety and effectiveness of the vaccines on social media like Twitter. In this study, such tweets are being extracted from Twitter using a Twitter API authentication token. The raw tweets are stored and processed using NLP. The processed data is then classified using a supervised KNN classification algorithm. The algorithm classifies the data into three classes, positive, negative, and neutral. These classes refer to the sentiment of the general people whose Tweets are extracted for analysis. From the analysis it is seen that Pfizer shows 47.29%positive, 37.5% negative and 15.21% neutral, Moderna shows 46.16%positive, 40.71% negative, and 13.13% neutral, AstraZeneca shows 40.08%positive, 40.06% negative and 13.86% neutral sentiment.

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Section: Methodsmentioning

confidence: 99%

“…The implementation worked for the proposed system is done on text data from Twitter tweets specifically the ones that are related to COVID-19 vaccines. To extract tweets [10], a Twitter developer account is mandatory. There Twitter keys/API credentials will be stored in variables and then create the authentication object.…”

Section: Data Collectionmentioning

confidence: 99%

Sentiment analysis on twitter tweets about COVID-19 vaccines usi ng NLP and supervised KNN classification algorithm

Shamrat

Chakraborty

Imran³

et al. 2021

IJEECS

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“…We performed four existing classification methods, i.e., random forest algorithm (RFA), artificial neural network (ANNs), support vector machine (SVM) and logistic regression (LR), to predict the disease status (i.e., KBD or normal). The classification models were trained with default parameter settings companying with four feature selection methods (i.e., RFA, mRMR [ 21 ], SVM-RFE [ 22 ] and Relief [ 23 ]), which are falling into three categories, i.e., wrappers, embedded methods, and filters [ 24 – 26 ]. All methods were implemented by Python (Version 3.6.10) within sklearn framework (v 0.23.1).…”

Section: Methodsmentioning

confidence: 99%

Identifying discriminative features for diagnosis of Kashin-Beck disease among adolescents

Zhang

Wei

Cao

et al. 2021

BMC Musculoskelet Disord

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Introduction Diagnosing Kashin-Beck disease (KBD) involves damages to multiple joints and carries variable clinical symptoms, posing great challenge to the diagnosis of KBD for clinical practitioners. However, it is still unclear which clinical features of KBD are more informative for the diagnosis of Kashin-Beck disease among adolescent. Methods We first manually extracted 26 possible features including clinical manifestations, and pathological changes of X-ray images from 400 KBD and 400 non-KBD adolescents. With such features, we performed four classification methods, i.e., random forest algorithms (RFA), artificial neural networks (ANNs), support vector machines (SVMs) and linear regression (LR) with four feature selection methods, i.e., RFA, minimum redundancy maximum relevance (mRMR), support vector machine recursive feature elimination (SVM—RFE) and Relief. The performance of diagnosis of KBD with respect to different classification models were evaluated by sensitivity, specificity, accuracy, and the area under the receiver operating characteristic (ROC) curve (AUC). Results Our results demonstrated that the 10 out of 26 discriminative features were displayed more powerful performance, regardless of the chosen of classification models and feature selection methods. These ten discriminative features were distal end of phalanges alterations, metaphysis alterations and carpals alterations and clinical manifestations of ankle joint movement limitation, enlarged finger joints, flexion of the distal part of fingers, elbow joint movement limitation, squatting limitation, deformed finger joints, wrist joint movement limitation. Conclusions The selected ten discriminative features could provide a fast, effective diagnostic standard for KBD adolescents.

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“…A customize trainable layer consisting of one hidden layer with 128 neurons was introduced at this stage. The Average Pooling operation was applied where the pool size is (7,7). The process is shown in Fig.…”

Section: F Classification Outcome Of Mobilenetv2 Modelmentioning

confidence: 99%

Deep Learning Predictive Model for Colon Cancer Patient using CNN-based Classification

Tasnim¹,

Chakraborty²,

Shamrat³

et al. 2021

IJACSA

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In recent years, the area of Medicine and Healthcare has made significant advances with the assistance of computational technology. During this time, new diagnostic techniques were developed. Cancer is the world's second-largest cause of mortality, claiming the lives of one out of every six individuals. The colon cancer variation is the most frequent and lethal of the numerous kinds of cancer. Identifying the illness at an early stage, on the other hand, substantially increases the odds of survival. A cancer diagnosis may be automated by using the power of Artificial Intelligence (AI), allowing us to evaluate more cases in less time and at a lower cost. In this research, CNN models are employed to analyse imaging data of colon cells. For colon cell image classification, CNN with max pooling and average pooling layers and MobileNetV2 models are utilized. To determine the learning rate, the models are trained and evaluated at various Epochs. It's found that the accuracy of the max pooling and average pooling layers is 97.49% and 95.48%, respectively. And MobileNetV2 outperforms the other two models with the most remarkable accuracy of 99.67% with a data loss rate of 1.24.

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Efficient Prediction of Cardiovascular Disease Using Machine Learning Algorithms With Relief and LASSO Feature Selection Techniques

Cited by 346 publications

References 69 publications

Sentiment analysis on twitter tweets about COVID-19 vaccines usi ng NLP and supervised KNN classification algorithm

Sentiment analysis on twitter tweets about COVID-19 vaccines usi ng NLP and supervised KNN classification algorithm

Identifying discriminative features for diagnosis of Kashin-Beck disease among adolescents

Deep Learning Predictive Model for Colon Cancer Patient using CNN-based Classification

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