publications

2024

1. J. Oper. Intell.

   A Comparative Analysis of the Machine Learning Methods for Predicting Diabetes

   Mohammad Maydanchi, Mehrbod Ziaei, Mehrdad Mohammadi, and 4 more authors

   Journal of Operations Intelligence. More Information can be found here , May 2024

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   Diabetes can lead to various health problems and complications, such as cardiovascular disease, kidney damage (nephropathy), eye issues, neuropathy, and foot ailments. Therefore, early diagnosis of diabetes can be immensely beneficial in preventing the development of these conditions. Utilizing machine learning is one method to detect diabetes in individuals at an early stage. In this study, we compare the performance of nine machine-learning classification models in predicting diabetes. These models include XGBoost, gradient boosting, AdaBoost, logistic regression, decision tree, KNN, perceptron, random forest, and naïve bayes. We utilize several evaluation metrics, focusing on the f1-score, area under the curve (AUC), and computational runtime. Our comparison reveals that complex tree-based models exhibit the highest f1-score and AUC, albeit with longer execution times.

   @article{jopi21202421,
     title = {A Comparative Analysis of the Machine Learning Methods for Predicting Diabetes},
     author = {Maydanchi, Mohammad and Ziaei, Mehrbod and Mohammadi, Mehrdad and Ziaei, Armin and Basiri, Mina and Haji, Fatemeh and Gharibi, Kazhal},
     journal = {Journal of Operations Intelligence},
     volume = {2},
     number = {1},
     pages = {230--251},
     year = {2024},
     month = may,
     publisher = {Scientific Oasis},
     doi = {10.31181/jopi21202421},
     url = {https://jopi-journal.org/index.php/jopi/article/view/21},
     dimensions = {true},
   }

2. Preprint

   A Comparison of Methods for Predicting Heart Disease: Neural Network, XGBoost, Gradient Boost, Logistic Regression, and SVM

   Mohammad Maydanchi, Mina Basiri, Armin Ziaei, and 3 more authors

   Under Review, May 2024

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   Cardiovascular diseases (CVD) are a major cause of mortality, demanding immediate attention. Early screening for risk factors speeds up CVD identification and treatment, reducing mortality risk efficiently. This study compares the predictive abilities of XGBoost, Gradient Boost, Neural Network, Logistic Regression, and SVM in forecasting CVD symptoms using CDC data from Kaggle. Evaluating against a heart disease risk approach, we assess model performance with AUC and F1-score for class 1 due to data imbalance. Additionally, we consider training run time as another metric for evaluation. XGBoost, Gradient Boost, and Logistic Regression excel in AUC and runtime, outperforming Neural Network and SVM significantly. The performances are quite similar when comparing the F1-scores, but Logistic Regression, XGBoost, and Gradient Boost still maintain their superiority. The paper recommends using Logistic Regression and boosting-based decision tree models for future heart disease prediction investigations.

   @article{maydanchi2024heart_disease_neural,
     title = {A Comparison of Methods for Predicting Heart Disease: Neural Network, XGBoost, Gradient Boost, Logistic Regression, and SVM},
     author = {Maydanchi, Mohammad and Basiri, Mina and Ziaei, Armin and Haji, Fatemeh and Ziaei, Mehrbod and Sargolzaei, Saman},
     journal = {Under Review},
     year = {2024},
   }

2023

1. SoutheastCon

   Comparative Study of Decision Tree, AdaBoost, Random Forest, Naïve Bayes, KNN, and Perceptron for Heart Disease Prediction

   Mohammad Maydanchi, Armin Ziaei, Mina Basiri, and 5 more authors

   In SoutheastCon 2023, Apr 2023

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   Globally, cardiovascular diseases (CVD) are estimated to account for more than 32% of all deaths. Consequently, CVD has become a global health problem, and timely diagnosis is essential (WHO, 2021). Screening for risk factors accelerates the diagnosis and management of CVD, resulting in a more effective and rapid response, reducing the risk of death. This article compares six classification models, AdaBoost, Random Forest, Decision Tree, KNN, Naive Bayes, and Perceptron, to predict CVD symptoms. Based on CDC data collected from Kaggle, classification models were compared with the approach of examining effective factors to predict heart disease. Since the data set was imbalanced, the study performance was measured by AUC and F1-score in Class 1, which is the critical class in this dataset. AdaBoost is found to have the highest AUC and F1-score, respectively, of 0.828 and 0.37, while Decision Tree has the lowest AUC of 0.595 and F1-score of 0.25.

   @inproceedings{maydanchi2023heart_disease_comparison,
     title = {Comparative Study of Decision Tree, AdaBoost, Random Forest, Naïve Bayes, KNN, and Perceptron for Heart Disease Prediction},
     author = {Maydanchi, Mohammad and Ziaei, Armin and Basiri, Mina and Norouzi Azad, Alireza and Pouya, Shaheen and Ziaei, Mehrbod and Haji, Fatemeh and Sargolzaei, Saman},
     booktitle = {SoutheastCon 2023},
     pages = {204--208},
     year = {2023},
     month = apr,
     organization = {IEEE},
     doi = {10.1109/SoutheastCon51012.2023.10115189},
     url = {https://doi.org/10.1109/SoutheastCon51012.2023.10115189},
   }