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COMPARISON OF MACHINE LEARNING ALGORITHMS FOR HEART DISEASE PREDICTION

Yıl 2024, Cilt: 7 Sayı: 1, 133 - 146, 29.08.2024
https://doi.org/10.56809/icujtas.1433853

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.

Kaynakça

  • analysis of state-of-art classification models in an it incident severity prediction framework. Applied Sciences, 13(6), 3843.
  • Alexander Fillbrunn, Christian Dietz a, Julianus Pfeuffer, René Rahn, Gregory A. Landrum, Michael R. Berthold . (2017). KNIME for reproducible cross-domain analysis of life science data. Journal of Biotechnology, pp. 1-8.
  • Ashok Kumar Dwivedi. (2018). Performance evaluation of different machine learning techniques for prediction of heart disease. Neural Comput & Applic 29, 685–693.
  • Banaei, N., Moshfegh, J., Mohseni-Kabir, A., Houghton, J. M., Sun, Y., & Kim, B. (2019). Machine learning algorithms enhance the specificity of cancer biomarker detection using SERS-based immunoassays in microfluidic chips. RSC advances, 9(4), 1859-1868.
  • Bernd Wiswedel, M. B. (2009). knime. (software) Retrieved from https://www.knime.com/.
  • Bhardwaj, R., Nambiar, A. R., & Dutta, D. (2017, July). A study of machine learning in healthcare. In 2017 IEEE 41st annual computer software and applications conference (COMPSAC) (Vol. 2, pp. 236-241). IEEE.
  • Dr. M. Kavitha, G. Gnaneswar, R. Dinesh, Y. R. Sai and R. S. Suraj. (2021). Heart Disease Prediction using Hybrid machine Learning Model. Coimbatore, India: 2021 6th International Conference on Inventive Computation Technologies (ICICT).
  • F. -J. Yang. (2018). An Implementation of Naive Bayes Classifier. International Conference on Computational Science and Computational Intelligence (CSCI), Las Vegas, NV, USA, 2018, pp. 301-306.
  • Ferdous, M., Debnath, J., & Chakraborty, N. R. (2020, July). Machine learning algorithms in healthcare: A literature survey. In 2020 11th International conference on computing, communication and networking technologies (ICCCNT) (pp. 1-6). IEEE.
  • G. S. Sajja, M. Mustafa, K. Phasinam, K. Kaliyaperumal, R. J. M. Ventayen and T. Kassanuk, (2021). Towards Application of Machine Learning in Classification and Prediction of Heart Disease. 2021 Second International Conference on Electronics and Sustainable Communication Systems (ICESC), 1664-1669.
  • García, S., Ramírez-Gallego, S., Luengo, J., Benítez, J. M., & Herrera, F. (2016). Big data preprocessing: methods and prospects. Big Data Analytics, 1(1), pp. 1-22.
  • Hand, D. J. (2007). Principles of Data Mining. Drug Safety, pp. 1-30.
  • Haziemeh, F.A., Darawsheh, S.R., Alshurideh, M., Al-Shaar, A.S. (2023). Using Logistic Regression Approach to Predicating Breast Cancer DATASET. The Effect of Information Technology on Business and Marketing Intelligence Systems, pp. 1-10.
  • Hossain, M. a. (2015). A review of evaluation metrics for data classification evaluations. International Journal of Data Mining & Knowledge Management Process (IJDKP), pp. 1-11.
  • J. P. Li, A. U. Haq, S. U. Din, J. Khan, A. Khan, and A. Saboor, (2020). Heart Disease Identification Method Using Machine Learning Classification in E-Healthcare. IEEE Access, 107562-107582.
  • M. Ferdous, J. Debnath and N. R. Chakraborty. (2020). Machine Learning Algorithms in Healthcare: A Literature Survey. 11th International Conference on Computing, Communication and Networking Technologies (ICCCNT), pp. (1-6).
  • M.R. Berthold, N. Cebron, F. Dill, T.R. Gabriel, T. Kötter, T. Meinl, P. Ohl, C. Sieb, K. Thiel, B. Wiswedel. (2009). KNIME: the Konstanz information miner. ACM SIGKDD Explorations Newsletter, 6 pages.
  • Ma, J., Ding, Y., Cheng, J. C., Tan, Y., Gan, V. J., & Zhang, J. (2019). Analyzing the leading causes of traffic fatalities using XGBoost and grid-based analysis: a city management perspective. IEEE Access, 7, 148059-148072.
  • Mahesh, B. (2020). Machine learning algorithms—a review. Int. J. Sci., 5.
  • Maryam I. Al-Janabi, , Mahmoud H. Qutqut and , Mohammad Hijjawi. (2018). Machine Learning Classification Techniques for Heart Disease Prediction: A Review. International Journal of Engineering & Technology, 7 (4) (2018) 5373-5379.
  • Md Faisal Kabir, Tianjie Chen, Simone A. Ludwig. (2023). A performance analysis of dimensionality reduction algorithms in machine learning models for cancer prediction. Healthcare Analytics, 9 pages.
  • Md Mamun Ali, Bikash Kumar Paul, Kawsar Ahmed, Francis M. Bui , Julian M.W. Quinn , Mohammad Ali Moni .(2021). Heart disease prediction using supervised machine learning algorithms: Performance analysis and comparison. Computers in Biology and Medicine, 10 pages.
  • Medjahed, S. A., Saadi, T. A., & Benyettou, A. (2013). Breast cancer diagnosis by using k-nearest neighbor with different distances and classification rules. International Journal of Computer Applications, 62(1).
  • Meysam Vakili, Mohammad Ghamsari and Masoumeh Rezaei. (2020). Performance Analysis and Comparison of Machine and Deep Learning Algorithms for IoT Data Classification. 13 pages.
  • Niyati Gupta, Arushi Rawal, Dr. V.L. Narasimhan, Savita Shiwani. (2013). Accuracy, Sensitivity and Specificity Measurement of Various Classification Techniques on Healthcare Data. IOSR Journal of Computer Engineering (IOSR-JCE), pp 70-73.
  • Patil, T. R. (2013). Performance Analysis of Naive Bayes and J48 Classification Algorithm for Data Classification. Int. J. Comput. Sci. Appl., 6.
  • Pavan Kumar T and Avinash Golande. (2019). Heart Disease Prediction Using Efficient Machine Learning Methods. International Journal of Current Technology, 70.
  • PYTLAK, K. (2020). kaggle. (Kaggle) Retrieved from https://www.kaggle.com/datasets/kamilpytlak/personal-key-indicators-of-heart-disease?select=heart_2020_cleaned.csv.
  • Rajeswari R. P, Kavitha Juliet, Dr. Aradhana. (2017). Text Classification for Student Data Set using Naive Bayes Classifier and KNN Classifier. International Journal of Computer Trends and Technology (IJCTT), pp. 1-5.
  • Ramesh TR, Umesh Kumar Lilhore, Poongodi M, Sarita Simaiya, Amandeep Kaur and Mounir Hamdi. (2022). predictive analysis of heart diseases with machine learning approaches. Malaysian Journal of Computer Science, 132–148.
  • Rymarczyk, T., Kozłowski, E., Kłosowski, G., & Niderla, K. (2019). Logistic regression for machine learning in process tomography. Sensors, 19(15), 3400.
  • Samir S Yadav; Shivajirao M. Jadhav; Snigdha Nagrale; Niraj Patil. (2020). Application of Machine Learning for the Detection of Heart Disease. 2020 2nd International Conference on Innovative Mechanisms for Industry Applications (ICIMIA), 165-172.
  • T. Vivekanandan, N. Ch Sriman Narayana Iyengar. (2017). Optimal feature selection using a modified differential evolution algorithm and its effectiveness for prediction of heart disease, Computers in Biology and Medicine, pp 125-136.
  • Tauben Averbuch, Kristen Sullivan, Andrew Sauer, Mamas A Mamas, Adriaan A. Voors, Chris P. Gale, Marco Metra, Neal Ravindra and Harriette G.C. Van Spall. (2022). Applications of artificial intelligence and machine learning in heart failure. European Heart Journal - Digital Health, 311-322.
  • Tougui, I., Jilbab, A. & El Mhamdi, J. (2020). Heart disease classification using data mining tools and machine learning techniques. Health Technol, 1137–1144.
  • Uddin, S., Haque, I., Lu, H. et al. (2022). Comparative performance analysis of K-nearest neighbour (KNN) algorithm and its different variants for disease prediction. Sci Rep 12, 6256.
  • Umarani Nagavelli, Debabrata Samanta and Partha Chakraborty. (2022). Machine Learning Technology-Based Heart Disease Detection Models. Journal of Healthcare Engineering, 9 pages.

KALP HASTALIĞI TAHMİNİNDE MAKİNE ÖĞRENİMİ ALGORİTMALARININ PERFORMANS KARŞILAŞTIRMASI

Yıl 2024, Cilt: 7 Sayı: 1, 133 - 146, 29.08.2024
https://doi.org/10.56809/icujtas.1433853

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.

Kaynakça

  • analysis of state-of-art classification models in an it incident severity prediction framework. Applied Sciences, 13(6), 3843.
  • Alexander Fillbrunn, Christian Dietz a, Julianus Pfeuffer, René Rahn, Gregory A. Landrum, Michael R. Berthold . (2017). KNIME for reproducible cross-domain analysis of life science data. Journal of Biotechnology, pp. 1-8.
  • Ashok Kumar Dwivedi. (2018). Performance evaluation of different machine learning techniques for prediction of heart disease. Neural Comput & Applic 29, 685–693.
  • Banaei, N., Moshfegh, J., Mohseni-Kabir, A., Houghton, J. M., Sun, Y., & Kim, B. (2019). Machine learning algorithms enhance the specificity of cancer biomarker detection using SERS-based immunoassays in microfluidic chips. RSC advances, 9(4), 1859-1868.
  • Bernd Wiswedel, M. B. (2009). knime. (software) Retrieved from https://www.knime.com/.
  • Bhardwaj, R., Nambiar, A. R., & Dutta, D. (2017, July). A study of machine learning in healthcare. In 2017 IEEE 41st annual computer software and applications conference (COMPSAC) (Vol. 2, pp. 236-241). IEEE.
  • Dr. M. Kavitha, G. Gnaneswar, R. Dinesh, Y. R. Sai and R. S. Suraj. (2021). Heart Disease Prediction using Hybrid machine Learning Model. Coimbatore, India: 2021 6th International Conference on Inventive Computation Technologies (ICICT).
  • F. -J. Yang. (2018). An Implementation of Naive Bayes Classifier. International Conference on Computational Science and Computational Intelligence (CSCI), Las Vegas, NV, USA, 2018, pp. 301-306.
  • Ferdous, M., Debnath, J., & Chakraborty, N. R. (2020, July). Machine learning algorithms in healthcare: A literature survey. In 2020 11th International conference on computing, communication and networking technologies (ICCCNT) (pp. 1-6). IEEE.
  • G. S. Sajja, M. Mustafa, K. Phasinam, K. Kaliyaperumal, R. J. M. Ventayen and T. Kassanuk, (2021). Towards Application of Machine Learning in Classification and Prediction of Heart Disease. 2021 Second International Conference on Electronics and Sustainable Communication Systems (ICESC), 1664-1669.
  • García, S., Ramírez-Gallego, S., Luengo, J., Benítez, J. M., & Herrera, F. (2016). Big data preprocessing: methods and prospects. Big Data Analytics, 1(1), pp. 1-22.
  • Hand, D. J. (2007). Principles of Data Mining. Drug Safety, pp. 1-30.
  • Haziemeh, F.A., Darawsheh, S.R., Alshurideh, M., Al-Shaar, A.S. (2023). Using Logistic Regression Approach to Predicating Breast Cancer DATASET. The Effect of Information Technology on Business and Marketing Intelligence Systems, pp. 1-10.
  • Hossain, M. a. (2015). A review of evaluation metrics for data classification evaluations. International Journal of Data Mining & Knowledge Management Process (IJDKP), pp. 1-11.
  • J. P. Li, A. U. Haq, S. U. Din, J. Khan, A. Khan, and A. Saboor, (2020). Heart Disease Identification Method Using Machine Learning Classification in E-Healthcare. IEEE Access, 107562-107582.
  • M. Ferdous, J. Debnath and N. R. Chakraborty. (2020). Machine Learning Algorithms in Healthcare: A Literature Survey. 11th International Conference on Computing, Communication and Networking Technologies (ICCCNT), pp. (1-6).
  • M.R. Berthold, N. Cebron, F. Dill, T.R. Gabriel, T. Kötter, T. Meinl, P. Ohl, C. Sieb, K. Thiel, B. Wiswedel. (2009). KNIME: the Konstanz information miner. ACM SIGKDD Explorations Newsletter, 6 pages.
  • Ma, J., Ding, Y., Cheng, J. C., Tan, Y., Gan, V. J., & Zhang, J. (2019). Analyzing the leading causes of traffic fatalities using XGBoost and grid-based analysis: a city management perspective. IEEE Access, 7, 148059-148072.
  • Mahesh, B. (2020). Machine learning algorithms—a review. Int. J. Sci., 5.
  • Maryam I. Al-Janabi, , Mahmoud H. Qutqut and , Mohammad Hijjawi. (2018). Machine Learning Classification Techniques for Heart Disease Prediction: A Review. International Journal of Engineering & Technology, 7 (4) (2018) 5373-5379.
  • Md Faisal Kabir, Tianjie Chen, Simone A. Ludwig. (2023). A performance analysis of dimensionality reduction algorithms in machine learning models for cancer prediction. Healthcare Analytics, 9 pages.
  • Md Mamun Ali, Bikash Kumar Paul, Kawsar Ahmed, Francis M. Bui , Julian M.W. Quinn , Mohammad Ali Moni .(2021). Heart disease prediction using supervised machine learning algorithms: Performance analysis and comparison. Computers in Biology and Medicine, 10 pages.
  • Medjahed, S. A., Saadi, T. A., & Benyettou, A. (2013). Breast cancer diagnosis by using k-nearest neighbor with different distances and classification rules. International Journal of Computer Applications, 62(1).
  • Meysam Vakili, Mohammad Ghamsari and Masoumeh Rezaei. (2020). Performance Analysis and Comparison of Machine and Deep Learning Algorithms for IoT Data Classification. 13 pages.
  • Niyati Gupta, Arushi Rawal, Dr. V.L. Narasimhan, Savita Shiwani. (2013). Accuracy, Sensitivity and Specificity Measurement of Various Classification Techniques on Healthcare Data. IOSR Journal of Computer Engineering (IOSR-JCE), pp 70-73.
  • Patil, T. R. (2013). Performance Analysis of Naive Bayes and J48 Classification Algorithm for Data Classification. Int. J. Comput. Sci. Appl., 6.
  • Pavan Kumar T and Avinash Golande. (2019). Heart Disease Prediction Using Efficient Machine Learning Methods. International Journal of Current Technology, 70.
  • PYTLAK, K. (2020). kaggle. (Kaggle) Retrieved from https://www.kaggle.com/datasets/kamilpytlak/personal-key-indicators-of-heart-disease?select=heart_2020_cleaned.csv.
  • Rajeswari R. P, Kavitha Juliet, Dr. Aradhana. (2017). Text Classification for Student Data Set using Naive Bayes Classifier and KNN Classifier. International Journal of Computer Trends and Technology (IJCTT), pp. 1-5.
  • Ramesh TR, Umesh Kumar Lilhore, Poongodi M, Sarita Simaiya, Amandeep Kaur and Mounir Hamdi. (2022). predictive analysis of heart diseases with machine learning approaches. Malaysian Journal of Computer Science, 132–148.
  • Rymarczyk, T., Kozłowski, E., Kłosowski, G., & Niderla, K. (2019). Logistic regression for machine learning in process tomography. Sensors, 19(15), 3400.
  • Samir S Yadav; Shivajirao M. Jadhav; Snigdha Nagrale; Niraj Patil. (2020). Application of Machine Learning for the Detection of Heart Disease. 2020 2nd International Conference on Innovative Mechanisms for Industry Applications (ICIMIA), 165-172.
  • T. Vivekanandan, N. Ch Sriman Narayana Iyengar. (2017). Optimal feature selection using a modified differential evolution algorithm and its effectiveness for prediction of heart disease, Computers in Biology and Medicine, pp 125-136.
  • Tauben Averbuch, Kristen Sullivan, Andrew Sauer, Mamas A Mamas, Adriaan A. Voors, Chris P. Gale, Marco Metra, Neal Ravindra and Harriette G.C. Van Spall. (2022). Applications of artificial intelligence and machine learning in heart failure. European Heart Journal - Digital Health, 311-322.
  • Tougui, I., Jilbab, A. & El Mhamdi, J. (2020). Heart disease classification using data mining tools and machine learning techniques. Health Technol, 1137–1144.
  • Uddin, S., Haque, I., Lu, H. et al. (2022). Comparative performance analysis of K-nearest neighbour (KNN) algorithm and its different variants for disease prediction. Sci Rep 12, 6256.
  • Umarani Nagavelli, Debabrata Samanta and Partha Chakraborty. (2022). Machine Learning Technology-Based Heart Disease Detection Models. Journal of Healthcare Engineering, 9 pages.
Toplam 37 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular Örüntü Tanıma
Bölüm Research Article
Yazarlar

Ayat Bahaa Abdulhussein 0000-0001-6511-8171

Turgay Tugay Bilgin 0000-0002-9245-5728

Yayımlanma Tarihi 29 Ağustos 2024
Gönderilme Tarihi 8 Şubat 2024
Kabul Tarihi 13 Nisan 2024
Yayımlandığı Sayı Yıl 2024 Cilt: 7 Sayı: 1

Kaynak Göster

APA Abdulhussein, A. B., & Bilgin, T. T. (2024). COMPARISON OF MACHINE LEARNING ALGORITHMS FOR HEART DISEASE PREDICTION. İstanbul Ticaret Üniversitesi Teknoloji Ve Uygulamalı Bilimler Dergisi, 7(1), 133-146. https://doi.org/10.56809/icujtas.1433853