Genetik Algoritma Yaklaşımıyla Öznitelik Seçimi Kullanılarak Makine Öğrenmesi Algoritmaları ile Kalp Hastalığı Tahmini

Vatansever, Berna; Aydın, Hakan

doi:10.53525/jster.1005934

Cited by 3 publications

(1 citation statement)

References 40 publications

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“…In the study conducted by Vatansever et al, Decision Tree, Logistic Regression, k-NN, Random Forest, Support Vector Machine and Naive Bayes models were used to predict heart disease with various machine learning algorithms by selecting features with Genetic Algorithm approach. As a result of the study, the highest success rate was obtained with 93.44% in the experiments with the genetic algorithm approach [17]. In the study conducted by Salman and Aksoy, hyperparameter selection was performed with the Random Forest algorithm and then feature selection was performed using the binary particle swarm intelligence method.…”

Section: Related Studies In the Literaturementioning

confidence: 96%

Early-stage heart failure disease prediction with deep learning approach

DEMİR,

BOZKURT,

AYIK

2023

JSR-A

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Cardiovascular diseases rank the highest among diseases in terms of mortality rate and cause millions of deaths every year. Heart failure is a type of cardiovascular disease and its early diagnosis is extremely important for its prevention. It may be vitally important to understand to what extent which body values, characteristics and factors (age, gender, blood pressure, sugar, etc.) affect this disease and to predict whether the individual will have a possible heart attack in the future. In this study, firstly, the correlation level of the relevant body values with the disease is extracted and in the second stage, a method that predicts heart attack with DNN (Deep Neural Network) and CNN (Convolutional Neural Network) deep learning models is proposed. In the study, 918 observations obtained from the kaggle site were used. Firstly, missing data, categorical data, non-numerical features were checked. Then, outliers were cleaned and the relationship of the features in the dataset with the disease state was revealed by feature engineering operations on the data. Finally, deep neural network models were built and the model was trained and hyperparameter adjustment was performed with GridSearhCV to achieve the highest success rate. As a result of the study, Accuracy, Precision, Recall and F1-Score values were found as 0.9375, 0.9629, 0.9176, 0.9397 for DNN and 0.9312, 0.9512, 0.9176, 0.9340 for CNN respectively. The AUC value calculated from the ROC curve was found to be equal to 0.96 in both deep learning models.

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Section: Related Studies In the Literaturementioning

confidence: 96%

Early-stage heart failure disease prediction with deep learning approach

DEMİR,

BOZKURT,

AYIK

2023

JSR-A

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Rule-Based Cardiovascular Disease Diagnosis

ÜNLÜ>

Kandaz

Çağıl

et al. 2023

Engineering Cyber-Physical Systems and Critical Infrastructures

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Conventional Machine Learning and Ensemble Learning Techniques in Cardiovascular Disease Prediction and Analysis

Kazangirler,

Özkaynak

2024

jista

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Cardiovascular diseases, which significantly affect the heart and blood vessels, are one of the leading causes of death worldwide. Early diagnosis and treatment of these diseases, which cause approximately 19.1 million deaths, are essential. Many problems, such as coronary artery disease, blood vessel disease, irregular heartbeat, heart muscle disease, heart valve problems, and congenital heart defects, are included in this disease definition. Today, researchers in the field of cardiovascular disease are using approaches based on diagnosis-oriented machine learning. In this study, feature extraction is performed for the detection of cardiovascular disease, and classification processes are performed with a Support Vector Machine, Naive Bayes, Decision Tree, K-Nearest Neighbor, Bagging Classifier, Random Forest, Gradient Boosting, Logistic Regression, AdaBoost, Linear Discriminant Analysis and Artificial Neural Networks methods. A total of 918 observations from Cleveland, Hungarian Institute of Cardiology, University Hospitals of Switzerland, and Zurich, VA Medical Center were included in the study. Principal Component Analysis, a dimensionality reduction method, was used to reduce the number of features in the dataset. In the experimental findings, feature increase with artificial variables was also performed and used in the classifiers in addition to feature reduction. Support Vector Machines, Decision Trees, Grid Search Cross Validation, and existing various Bagging and Boosting techniques have been used to improve algorithm performance in disease classification. Gaussian Naïve Bayes was the highest-performing algorithm among the compared methods, with 91.0% accuracy on a weighted average basis as a result of a 3.0% improvement.

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Genetik Algoritma Yaklaşımıyla Öznitelik Seçimi Kullanılarak Makine Öğrenmesi Algoritmaları ile Kalp Hastalığı Tahmini

Cited by 3 publications

References 40 publications

Early-stage heart failure disease prediction with deep learning approach

Early-stage heart failure disease prediction with deep learning approach

Rule-Based Cardiovascular Disease Diagnosis

Conventional Machine Learning and Ensemble Learning Techniques in Cardiovascular Disease Prediction and Analysis

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