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All Journal Sinkron : Jurnal dan Penelitian Teknik Informatika Advance Sustainable Science, Engineering and Technology (ASSET)
Putri, Rusyda Tsaniya Eka
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Chemical Engineering, Chemistry & Bioengineering Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Energy Materials Science & Nanotechnology

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GridSearch and Data Splitting for Effectiveness Heart Disease Classification Putri, Rusyda Tsaniya Eka; Junta Zeniarja; Sri Winarno; Ailsa Nurina Cahyani; Ahmad Alaik Maulani
Sinkron : jurnal dan penelitian teknik informatika Vol. 8 No. 1 (2024): Articles Research Volume 8 Issue 1, January 2024
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v9i1.13198

Abstract

Cardiovascular disease (CVD) is a major global health issue that affects death rates significantly. This research aims to improve the early detection and diagnosis of cardiovascular illness by utilizing machine learning methods, particularly classification algorithms. According to estimates from the World Health Organization (WHO), cardiovascular disease (CVD) caused 17.9 million deaths globally in 2019, or 32% of all fatalities. The treatment and prognosis of cardiovascular illness are greatly improved by early detection and diagnosis. Classification, in particular, machine learning, has become a prominent tool for solving problems connected to heart disease. The main objective of this project is to assess how well Grid Search and various data-sharing methods classify cardiac disease. SVM, Random Forest Classifier, Logistic Regression, Naïve Bayes, Decision Tree Classifier, KNN, and XGBoost Classifier are just a few machine learning methods. The UCI heart disease dataset, which contains information from 303 heart disease patients and 165 healthy participants, is used for the evaluation. Performance parameters like recall, accuracy, precision, and F1 score are considered to evaluate the algorithms' efficacy. The investigation's expected outcomes are intended to increase doctors' ability to diagnose cardiac disease more accurately. Moreover, these results may aid in creating more complex classification models for diagnosing cardiac conditions.
Comparison of Hyperparameter Optimization Techniques in Hybrid CNN-LSTM Model for Heart Disease Classification Maulani, Ahmad Alaik; Winarno, Sri; Zeniarja, Junta; Putri, Rusyda Tsaniya Eka; Cahyani, Ailsa Nurina
Sinkron : jurnal dan penelitian teknik informatika Vol. 8 No. 1 (2024): Articles Research Volume 8 Issue 1, January 2024
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v9i1.13219

Abstract

Heart disease, which causes the highest number of deaths worldwide, recorded about 17.9 million cases in 2019, or about 32% of total global deaths, according to the World Health Organization (WHO). The significance of early detection of heart disease drives research to develop effective diagnosis systems utilizing machine learning. The advancement of machine learning in healthcare currently primarily serves as a supporting role in the ability of clinicians or analysts to fulfill their roles, identify healthcare trends, and develop disease prediction models. Meanwhile, deep learning has experienced rapid development and has become the most popular method in recent years, one of which is detecting diseases. The main objective of this research is to optimize the hybrid convolutional neural network (CNN) and long short-term memory (LSTM) model for classifying heart disease by comparing hyperparameter optimization using grid search and random search. Although random search requires less time in hyperparameter tuning, the classification performance results of grid search show higher accuracy. In the test, the hybrid CNN and LSTM model with grid search achieved 91.67% accuracy, 89.66% recall (sensitivity), 93.55% specificity, 92.86% precision, 91.23% f1-score, and 0.9310 AUC value. These results confirm that using a hybrid CNN and LSTM model with a grid search approach is better suited for classifying heart disease.
Heart Disease Classification Using Deep Neural Network with SMOTE Technique for Balancing Data Cahyani, Ailsa Nurina; Zeniarja, Junta; Winarno, Sri; Putri, Rusyda Tsaniya Eka; Maulani, Ahmad Alaik
Advance Sustainable Science, Engineering and Technology Vol 6, No 1 (2024): November-January
Publisher : Universitas PGRI Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26877/asset.v6i1.17521

Abstract

Heart disease is the leading cause of premature death worldwide. According to the WHO, heart disease causes about 30% of the total 58 million deaths and mostly occurs in individuals who are in their productive age. This condition can occur to anyone, including individuals who do not show symptoms of heart disease. However, heart disease can be prevented with early detection. By understanding the various risk factors that can increase the potential for heart disease. Therefore, this study aims to classify heart disease using Deep Neural Network algorithm and SMOTE technique to overcome data imbalance. This research resulted in a validation accuracy of 90% with precision evaluation of 0.85, recall 0.92, and f1-score 0.88. Based on the results obtained, the Deep Neural Network algorithm after SMOTE is superior to the model without SMOTE.
Co-Authors Ahmad Alaik Maulani Ailsa Nurina Cahyani Cahyani, Ailsa Nurina Junta Zeniarja Maulani, Ahmad Alaik Sri Winarno Sri Winarno