KALP HASTALIĞI TAHMİNİNDE MAKİNE ÖĞRENİMİ ALGORİTMALARININ PERFORMANS KARŞILAŞTIRMASI

Öz

Makine öğrenimi, araştırma dünyasını değiştiren, yapay zekanın en bilinen uygulamalarından biridir. Bu araştırmanın hedefi, etkili makine öğrenimi yaklaşımlarını kullanarak Kalp Hastalığı Tahmini için tahminler üretmek ve kişinin kalp hastalığına sahip olup olmadığını tahmin etmektir. Temel amaç, çeşitli makine öğrenimi algoritmalarının kalp hastalığının varlığını veya yokluğunu tahmin etmedeki öngörü doğruluğunu değerlendirmektir. KNIME veri analizi programı genel doğruluk, bu stratejilerin etkinliğini değerlendirmek için temel gösterge olarak seçilmiştir. Göğüs ağrısı, kolesterol seviyeleri, bir kişinin yaşı ve diğer faktörler gibi detaylar kullanılarak ve K En Yakın Komşu (KNN), Naif Bayes ve Lojistik Regresyon gibi farklı makine öğrenimi teknolojileri kullanılarak, 319796 hasta kaydı ve 18 niteliğe sahip bir veri seti kullanılmıştır. Makine öğrenimi teknikleri olarak Naive Bayes, K En Yakın Komşu (KNN) ve Lojistik Regresyon kullanılmış ve tahmin doğrulukları karşılaştırılmıştır. Uygulama sonuçları, lojistik regresyon yaklaşımının kalp hastalığı için tahmin doğruluğu açısından K En Yakın Komşu yönteminden ve Naive Bayes yönteminden daha iyi olduğunu göstermektedir. K-NN'nin tahmin doğruluğu %90.77, Naive Bayes'in %86.633 ve lojistik regresyonun %91.60'dır. Sonuç olarak, makine öğrenimi algoritmalarının kalp hastalığını büyük oranda doğru bir şekilde tanımlayabileceği görülmüştür. Sonuçlar, bu yöntemlerin bir hastada kalp krizi olasılığını belirlemede doktorlara ve kalp cerrahlarına yardımcı olabileceğini göstermektedir.

Anahtar Kelimeler

• Naïve Bayes Algoritması
• Lojistik Regresyon
• K En Yakın Komşu
• kalp hastalığı.

COMPARISON OF MACHINE LEARNING ALGORITHMS FOR HEART DISEASE PREDICTION

Öz

Machine learning, one of the most well-known applications of artificial intelligence, is altering the world of research. The aim of this study is to generate predictions for Heart Disease Prediction (HDP) by employing effective machine learning approaches and to predict whether an individual has heart disease. The primary objective is to evaluate the predictive accuracy of various machine learning algorithms in predicting the presence or absence of heart disease. The KNIME data analysis program has been selected, and overall accuracy is chosen as the primary indicator to assess the effectiveness of these strategies. Utilizing details such as chest pain, cholesterol levels, age, and other factors, along with different machine learning technologies such as K Nearest Neighbor (KNN), Naive Bayes, and Logistic Regression, a dataset of 319,796 patient records with 18 attributes was utilized. Naive Bayes, K Nearest Neighbor (KNN), and Logistic Regression were employed as machine learning techniques, and their prediction accuracies were compared. The application results indicate that the logistic regression approach outperforms the K Nearest Neighbor method and the Naive Bayes method in terms of predicting accuracy for heart disease. The prediction accuracy of K-NN is 90.77%, Naive Bayes is 86.633%, and logistic regression is 91.60%. In conclusion, machine learning algorithms can accurately identify heart disease. The results suggest that these methods could assist doctors and heart surgeons in determining the likelihood of a heart attack in a patient.

Anahtar Kelimeler

• Naïve Bayes Algorithm
• Logistic Regression
• K Nearest Neighbor
• Heart Disease

Kaynakça

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