RETRACTED ARTICLE: Feature optimization by discrete weights for heart disease prediction using supervised learning

Al-Yarimi, Fuad Ali Mohammed; Munassar, Nabil Mohammed Ali; Bamashmos, Mohammed Hasan Mohammed; Ali, Mohammed Yousef Salem

doi:10.1007/s00500-020-05253-4

Cited by 35 publications

(29 citation statements)

References 17 publications

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“…The data content of the heart disease prediction consists of a collection of records, representing the diagnosis of the patient heart disease (Al-Yarimi et al. 2021 ). The extracted dataset was the Public Health dataset (Gangal 2021 ).…”

Section: Methodsmentioning

confidence: 99%

Quantum K-means clustering method for detecting heart disease using quantum circuit approach

Kavitha¹,

Kaulgud²

2022

Soft Comput

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The development of noisy intermediate- scale quantum computers is expected to signify the potential advantages of quantum computing over classical computing. This paper focuses on quantum paradigm usage to speed up unsupervised machine learning algorithms particularly the K-means clustering method. The main approach is to build a quantum circuit that performs the distance calculation required for the clustering process. This proposed technique is a collaboration of data mining techniques with quantum computation. Initially, extracted heart disease dataset is preprocessed and classical K-means clustering performance is evaluated. Later, the quantum concept is applied to the classical approach of the clustering algorithm. The comparative analysis is performed between quantum and classical processing to check performance metrics.

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Section: Methodsmentioning

confidence: 99%

Quantum K-means clustering method for detecting heart disease using quantum circuit approach

Kavitha¹,

Kaulgud²

2022

Soft Comput

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“…In this proposed work, for feature selection the labeled data are separated into two sets of positive and negative represented as matrix of PM and NM. Each row of these matrix represented as the recommended medical tests carried and each column represents that the result of these medical tests [23]. If the deviation between these two values is lower than threshold then the value obtained from the record will falls onto any of the label.…”

Section: Dgwo To Find Neighbor Anmentioning

confidence: 99%

Discrete GWO Optimized Data Aggregation for Reducing Transmission Rate in IoT

Devi¹,

Venkatachalam²,

Nam³

et al. 2023

Computer Systems Science and Engineering

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The conventional hospital environment is transformed into digital transformation that focuses on patient centric remote approach through advanced technologies. Early diagnosis of many diseases will improve the patient life. The cost of health care systems is reduced due to the use of advanced technologies such as Internet of Things (IoT), Wireless Sensor Networks (WSN), Embedded systems, Deep learning approaches and Optimization and aggregation methods. The data generated through these technologies will demand the bandwidth, data rate, latency of the network. In this proposed work, efficient discrete grey wolf optimization (DGWO) based data aggregation scheme using Elliptic curve Elgamal with Message Authentication code (ECEMAC) has been used to aggregate the parameters generated from the wearable sensor devices of the patient. The nodes that are far away from edge node will forward the data to its neighbor cluster head using DGWO. Aggregation scheme will reduce the number of transmissions over the network. The aggregated data are preprocessed at edge node to remove the noise for better diagnosis. Edge node will reduce the overhead of cloud server. The aggregated data are forward to cloud server for central storage and diagnosis. This proposed smart diagnosis will reduce the transmission cost through aggregation scheme which will reduce the energy of the system. Energy cost for proposed system for 300 nodes is 0.34μJ. Various energy cost of existing approaches such as secure privacy preserving data aggregation scheme (SPPDA), concealed data aggregation scheme for multiple application (CDAMA) and secure aggregation scheme (ASAS) are 1.3 μJ, 0.81 μJ and 0.51 μJ respectively. The optimization approaches and encryption method will ensure the data privacy.

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“…The approaches reviewed in the research were unable to achieve accurate diagnosis in case of unbalanced distribution of health records. Farman Ali [16] developed smart healthcare monitoring system for heart disease prediction based on ensemble deep learning and feature fusion. A conditional probability approach computed a specific feature weight for each class, which further improved system performance and the ensemble deep learning model, was trained for heart disease prediction.…”

Section: Literature Reviewmentioning

confidence: 99%

“…• F1-score: F-measure measures are statistical variability that performs Representation of (16) • Accuracy is defined as the ratio of correctly predicted to the total number of observations. The accuracy is calculated by using the Eq.…”

Section: Performance Measuresmentioning

confidence: 99%

A Big Data Analysis Using Fuzzy Deep Convolution Network Based Model for Heart Disease Classification

Manur¹,

Pani²,

Kumar³

2021

IJIES

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Heart disease is a serious disease that causes sudden death among 80% of the people around the world. The traditional models performed predictive analytics using machine learning techniques to make a better decision. For better decision making in heart disease prediction, the big data analysis shows the great opportunities to predict the future health status from health parameters and provide best outcomes. However, the traditional decision making models had traffic data or contained noise and uncertainty was unpredictable as the data ambiguity emerged. In order to overcome such an issue, the big data is used to ensure the medical service which is mostly needed in a timely manner and for accurate diagnosis. The pre-processing of the medical data acquired from Cleveland heart disease UCI datasets has a vast number of attributes which helps to predict the heart disease. The data are contaminated with the noise and some of the data are missing, so the pre-processing using Min max Normalization is performed to remove contaminated noise acquired in the data which is taken from the UCI repository dataset. The proposed Fuzzy Deep Convolution Network (FDCN) permits the input features for fuzzification process that uses transformed features. The fuzzification process eliminates the redundant or irrelevant fuzzified features and overcomes the system complexity problems. The proposed FDCN obtains accuracy of 95.56 % and 92 % of F-score shows better results when compared with the existing KNN-DT, Naive Bayes, and Random Forest algorithms.

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RETRACTED ARTICLE: Feature optimization by discrete weights for heart disease prediction using supervised learning

Cited by 35 publications

References 17 publications

Quantum K-means clustering method for detecting heart disease using quantum circuit approach

Quantum K-means clustering method for detecting heart disease using quantum circuit approach

Discrete GWO Optimized Data Aggregation for Reducing Transmission Rate in IoT

A Big Data Analysis Using Fuzzy Deep Convolution Network Based Model for Heart Disease Classification

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