Classification of Complete Myocardial Infarction Using Rule-Based Rough Set Method and Rough Set Explorer System

Halder, Basudev; Mitra, S.; Mitra, Madhuchhanda

doi:10.1080/03772063.2019.1588175

Cited by 21 publications

(4 citation statements)

References 23 publications

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“…Purposive sampling adalah metode yang digunakan dalam penelitian untuk memilih peserta atau sampel berdasarkan relevansinya dengan tujuan penelitian tertentu, yang meningkatkan keakuratan penelitian dan keandalan informasi dan hasil [10]. Data yang telah diperoleh kemudian dihitung nilai rata-ratanya yang kemudian akan dimasukkan ke dalam proses rough set menggunakan software R [11].…”

Section: Identifikasi Elemen Kemasanunclassified

Analisis Elemen Kemasan Sekunder Pembalut Dengan Metode Rough Set

Araminta,

Sari,

Muryeti

2024

SST

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A common problem often encountered in sanitary napkin packaging is inadequate product protection. Failure to open or reseal the packaging and the use of materials that cannot be reused are complaints from 97% of consumers regarding sanitary napkin products currently on the market. This research aims to analyze packaging design elements according to consumer preferences. In this research, the Kansei Engineering and Rough Set methods were used. Kansei Engineering has the advantage of being able to translate consumer emotions into design elements. The design element analysis process is carried out using the Rough Set method. The research results obtained consumer emotions in the form of 19 Kansei words and 29 packaging samples as design references. Analysis of sanitary napkin packaging design elements obtained recommendations in the form of cardboard material, folding cardboard packaging type, no additional features, box body, opening type with flap, medium size, feminine design, script typography, and image elements in the form of pictures and illustrations. This research succeeded in proving Kansei Engineering's ability to produce packaging designs according to consumer preferences through quantitative data analysis.Keywords - Model Kano, Rough Set, Design Element, Packaging

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Section: Identifikasi Elemen Kemasanunclassified

Analisis Elemen Kemasan Sekunder Pembalut Dengan Metode Rough Set

Araminta,

Sari,

Muryeti

2024

SST

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“…The outcomes demonstrated extraordinary performance, with average accuracy, sensitivity, and specificity values of 99.98%, 99.94%, and 99.98% for MI detection and 99.79%, 99.88%, and 99.98% for MI localization. The study [ 50 ] describes a computerized diagnostic system that detects and classifies five forms of MI from multi-lead ECG signals using a Rough Set classifier. The pathological ECG characteristics associated with MI are extracted, and an information table and knowledgebase are generated.…”

Section: Literature Reviewmentioning

confidence: 99%

Enhancing Diagnosis of Anterior and Inferior Myocardial Infarctions Using UWB Radar and AI-Driven Feature Fusion Approach

Zafar,

Siddiqui,

Majid

et al. 2023

Sensors

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Despite significant improvement in prognosis, myocardial infarction (MI) remains a major cause of morbidity and mortality around the globe. MI is a life-threatening cardiovascular condition that requires prompt diagnosis and appropriate treatment. The primary objective of this research is to identify instances of anterior and inferior myocardial infarction by utilizing data obtained from Ultra-wideband radar technology in a hospital for patients of anterior and inferior MI. The collected data is preprocessed to extract spectral features. A novel feature engineering approach is designed to fuse temporal features and class prediction probability features derived from the spectral feature dataset. Several well-known machine learning models are implemented and fine-tuned to obtain optimal performance in the detection of anterior and inferior MI. The results demonstrate that integration of the fused feature set with machine learning models results in a notable improvement in both the accuracy and precision of MI detection. Notably, random forest (RF) and k-nearest neighbor showed superb performance with an accuracy of 98.8%. For demonstrating the capacity of models to generalize, K-fold cross-validation is carried out, wherein RF exhibits a mean accuracy of 99.1%. Furthermore, the examination of computational complexity indicates a low computational complexity, thereby indicating computational efficiency.

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“…Subsequently, Halder er al. [21] implemented computerized diagnosis system using Rough set classifier from multi-lead ECG signal for the classification of myocardial infarction (MI) disease. Dwivedi [15] applied different algorithms, namely, ANN, SVM, logistic regression, k-nearest neighbors (KNN), classification tree, NB, and achieved the highest accuracy in logistic regression.…”

Section: Related Workmentioning

confidence: 99%

A hybrid GA and PSO optimized approach for heart-disease prediction based on random forest

El-Shafiey

Hagag

El‐Dahshan

et al. 2022

Multimed Tools Appl

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Nowadays, heart diseases are significantly contributing to deaths all over the world. Thus, heart-disease prediction has garnered considerable attention in the medical domain globally. Accordingly, machine-learning algorithms for the early prediction of heart diseases were developed in several studies to help physicians design medical procedures. In this study, a hybrid genetic algorithm (GA) and particle swarm optimization (PSO) optimized approach based on random forest (RF), called GAPSO-RF, is developed and used to select the optimal features that can increase the accuracy of heart-disease prediction. The proposed GAPSO-RF implements multivariate statistical analysis in the first step to select the most significant features used in the initial population. After that, a discriminate mutation strategy is implemented in GA. GAPSO-RF combines a modified GA for global search and a PSO for local search. Moreover, PSO achieved the concept of rehabbing individuals that had been refused in the selection process. The performance of the proposed GAPSO-RF approach is validated via evaluation metrics, namely, accuracy, specificity, sensitivity, and area under the receiver operating characteristic (ROC) curve by using two datasets from the University of California, namely, Cleveland and Statlog. The experimental results confirm that the GAPSO-RF approach attained the high heart-disease-prediction accuracies of 95.6% and 91.4% on the Cleveland and Statlog datasets, respectively. Furthermore, the proposed approach outperformed other state-of-the-art prediction methods.

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Classification of Complete Myocardial Infarction Using Rule-Based Rough Set Method and Rough Set Explorer System

Cited by 21 publications

References 23 publications

Analisis Elemen Kemasan Sekunder Pembalut Dengan Metode Rough Set

Analisis Elemen Kemasan Sekunder Pembalut Dengan Metode Rough Set

Enhancing Diagnosis of Anterior and Inferior Myocardial Infarctions Using UWB Radar and AI-Driven Feature Fusion Approach

A hybrid GA and PSO optimized approach for heart-disease prediction based on random forest

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