System Diagnosis of Coronary Heart Disease using A Combination of Dimensional Reduction and Data Mining Techniques: A Review

Wiharto, Wiharto; Kusnanto, Hari; Herianto, Herianto

doi:10.11591/ijeecs.v7.i2.pp514-523

Cited by 13 publications

(11 citation statements)

References 29 publications

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“…Data mining is also called knowledge discovery in database [10]. The problem of classification of diseases like this can be said included in the data mining case because it requires the existence of knowledge before it can be classified.…”

Section: Literature Studymentioning

confidence: 99%

Performance Analysis of Data Mining Methods for Sexually Transmitted Disease Classification

Yuliastuti¹,

Alfiyatin²,

Rizki³

et al. 2018

IJECE

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According to health reports of Malang city, many people are exposed to sexually transmitted diseases and most sufferers are not aware of the symptoms. Malang city being known as a city of education so that every year the population number increases, it is at risk of increasing the spread of sexually transmitted diseases virus. This problem is important to be solved to treat earlier sufferers sexually transmitted diseases virus in order to reduce the burden of patient spending. In this research, authors conduct data mining methods to classifying sexually transmitted diseases. From the experiment result shows that K-NN is the best method for solve this problem with 90% accuracy.

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Section: Literature Studymentioning

confidence: 99%

Performance Analysis of Data Mining Methods for Sexually Transmitted Disease Classification

Yuliastuti¹,

Alfiyatin²,

Rizki³

et al. 2018

IJECE

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“…The development of artificial intelligence is so fast and has been widely applied in various fields, including the health sector, one of which is for the diagnosis of coronary heart disease [5,6]. The development of intelligence system models for the diagnosis of coronary heart disease has been carried out, one of the stages is feature selection.…”

Section: Introductionmentioning

confidence: 99%

Framework Two-Tier Feature Selection on the Intelligence System Model for Detecting Coronary Heart Disease

Wiharto¹,

Suryani²,

Setyawan³

2021

ISI

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Coronary heart disease is a non-communicable disease with high mortality. A good action to anticipate this is to do prevention, namely by carrying out a healthy lifestyle and routine early examinations. Early detection of coronary heart disease requires a number of examinations, such as demographics, ECG, laboratory, symptoms, and even angiography. The number of inspection parameters in the context of early detection will have an impact on the time and costs that must be incurred. Selection of the right and important inspection parameters will save time and costs. This study proposes an intelligence system model for the detection of coronary heart disease by using a minimal examination attribute, with performance in the good category. This research method is divided into a number of stages, namely data normalization, feature selection, classification, and performance analysis. Feature selection uses a Two-tier feature selection framework consisting of correlation-based filters and wrappers. The system model is tested using a number of datasets, and classification algorithms. The test results show that the proposed two-tier feature selection framework is able to reduce the highest attribute of 73.51% in the z-Alizadeh Sani dataset. The performance of the system using the bagging-PART algorithm is able to provide the best performance with parameters area under the curve (AUC) 95.4%, sensitivity 95.9% while accuracy is 94.1%. Referring to the AUC value, the proposed system model is included in the good category.

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“…Lighting and weather conditions effect ALPR system significantly [7,8]. An ALPR system generally composed of four main blocks such as image capturing block, license plate detection block, character segmentation block and character recognition block, respectively [9][10][11][12]. The license plate detection, which is in the second block of the ALPD, can be seen as the most important part.…”

Section: Introductionmentioning

confidence: 99%

Fused faster RCNNs for efficient detection of the license plates

Omar

Abdulazeez

Şengür

et al. 2020

IJEECS

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Automatic License Plate Detection and Recognition (ALPD-R) is an important and challenging application for traffic surveillance, traffic safety, security, services purposes and parking management. Generally, traditional image processing routines have been used in ALPD-R. Although the general approaches perform well on ALPD-R, new and efficient approaches are needed to improve the detection accuracies. Thus, in this paper, a new approach, which is based on fusing of multiple Faster Regions with Convolutional Neutral Network (Faster- RCNN) architectures, is proposed. More specially, the Deep Learning (DL) is used to detect license plates in given images. The proposed license plate detection method uses three Faster- RCNN modules where each faster RCNN module uses a pre-trained CNN model namely AlexNet, VGG16 and VGG19. Each Faster-RCNN module is trained independently and their results are fused in fusing layer. Fusing layer use average operator on the X and Y coordinates of the outputs of the Faster-RCNN modules and maximum operator is employed on the width and height outputs of the Faster-RCNN modules. A publicly available dataset is used in experiments. The accuracy is used as a performance indicator of the proposed method. For 100 testing images, the proposed method detects the exact location of license plates for 97 images. The accuracy of the proposed method is 97%.

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System Diagnosis of Coronary Heart Disease using A Combination of Dimensional Reduction and Data Mining Techniques: A Review

Cited by 13 publications

References 29 publications

Performance Analysis of Data Mining Methods for Sexually Transmitted Disease Classification

Performance Analysis of Data Mining Methods for Sexually Transmitted Disease Classification

Framework Two-Tier Feature Selection on the Intelligence System Model for Detecting Coronary Heart Disease

Fused faster RCNNs for efficient detection of the license plates

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