یک رویکرد جدید مبتنی بر الگوریتم CatBoost و هوش مصنوعی تفسیرپذیر به منظور تشخیص بیماری کرونا بر اساس علائم بیماری

نوع مقاله : مقاله کامپیوتر

نویسندگان

1 استادیار گروه مهندسی کامپیوتر- دانشکده مهندسی برق و کامپیوتر دانشگاه سمنان

2 دانشکده مهندسی برق و کامپیوتر- دانشگاه سمنان

3 دانشکده مهندسی کامپیوتر- دانشگاه صنعتی امیرکبیر

چکیده

ویروس کرونا که در ماه دسامبر 2019 در شهر ووهان چین دیده شد و به سرعت در سراسر جهان شیوع پیدا کرد، همچنان یک تهدید مهم برای سلامت جهان به شمار می‌آید. علی‌رغم همه استراتژی‌های مورد استفاده برای مقابله با گسترش کویید-۱۹، هنوز به تدابیر بیشتری برای رفع پیامدهای ناشی از آن نیاز است. در این پژوهش برای تشخیص فرد مبتلا به کووید-۱۹ از ویژگی‌های بالینی افراد به عنوان داده‌های ورودی استفاده شده است که حاصل جمع‌آوری اطلاعات از پژوهش‌های مشابه است. همچنین از الگوریتمهای مختلفی شامل یادگیری ماشین بردار پشتیبان، رگرسیون لجستیک، k نزدیکترین همسایه (k=9)، بیز ساده، جنگل تصادفی، LightGBM، XgBoost و CatBoost استفاده شده که از میان آنها الگوریتمCatBoost ، با کسب حساسیت 97/97 درصد، دقت 72/97 درصد و صحت ۹۶/۹۶ درصد بهترین نتایج را از خود نشان داد. در این الگوریتم، برای تنظیم هر چه دقیقتر فوق‌پارامترها به منظور رسیدن به نتایج مطلوب از روش آزمون و خطا استفاده شده و از SHAP برای تفسیر نتایج و مشخص کردن تاثیر ویژگی‌ها بر خروجی الگوریتم استفاده گردیده است.

کلیدواژه‌ها

موضوعات


عنوان مقاله [English]

A novel approach based on CatBoost and explainable artificial intelligence for diagnosis of COVID-19 cases using patients' symptoms

نویسندگان [English]

  • Samaneh Emami 1
  • Ali SeyyedMomeni 2
  • Hamid Nasiri 3
1 Assistant Professor Of Department of Computer Hardware Engineering @ Faculty of Electrical & Computer Engineering
2 Department of Electrical and Computer Engineering, Semnan University, Semnan, Iran
3 Department of Computer Engineering, Amirkabir University of Technology (Tehran Polytechnic), Tehran, Iran
چکیده [English]

The COVID-19 virus, which was discovered in December 2019 in the city of Wuhan, China and quickly spread throughout the world, continues to be an important threat to the health of the world. Despite all the strategies used to deal with the spread of COVID-19, more contrivances are still needed to deal with its consequences. In this research, the clinical characteristics of people have been used as input data to diagnose a person with COVID-19, which is the result of collecting information from similar studies. Also, various algorithms including support vector machine, logistic regression, k nearest neighbor (k=9), simple bayes, random forest, LightGBM, XgBoost and CatBoost have been used, among which the CatBoost algorithm, with a sensitivity of 97.97%, accuracy 97.72% and 96.96% accuracy showed the best results. In this algorithm, the trial and error method has been used to adjust hyperparameters as accurately as possible to achieve the desired results, and SHAP is used to interpret the results and determine the impact of features on the output.

کلیدواژه‌ها [English]

  • CatBoost algorithm
  • Corona Virus
  • Deep Neural Network
  • Covid-19 Disease
  • Machine Learning
  • SHAP
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