Nội dung được dịch bởi AI, chỉ mang tính chất tham khảo
Phương pháp tích hợp mạng Bayesian và quy tắc kết hợp để tự động định hướng bệnh nhân COVID-19
Tóm tắt
Sự lây lan của virus corona vẫn tiếp tục diễn ra nhanh chóng trên toàn cầu, gây ra tác động tàn khốc đến sức khỏe của dân số toàn cầu. Để chống lại COVID-19, chúng tôi đề xuất một quy trình ra quyết định tự động mới kết hợp hai mô-đun nhằm hỗ trợ người ra quyết định: (1) mô-đun phân tích dữ liệu dựa trên phương pháp mạng Bayesian, được sử dụng để xác định mức độ nghiêm trọng của triệu chứng virus corona và phân loại các trường hợp thành nhẹ, trung bình và nặng, và (2) mô-đun ra quyết định tự động dựa trên phương pháp khai thác quy tắc kết hợp. Phương pháp này cho phép tự động tạo ra quyết định phù hợp dựa trên thuật toán FP-growth và khoảng cách giữa các đối tượng. Để xây dựng mô hình Mạng Bayesian, chúng tôi đề xuất một phương pháp mới dựa trên dữ liệu cho phép học một cách hiệu quả cấu trúc của mạng, cụ thể là thuật toán MIGT-SL. Các thí nghiệm được thực hiện trên tập dữ liệu rời rạc đã được xử lý trước. Thuật toán được đề xuất cho phép tạo ra đúng 74%, 87.5%, và 100% cấu trúc gốc của các mạng ALARM, ASIA, và CANCER. Mô hình Bayesian được đề xuất hoạt động tốt về độ chính xác với 96.15% và 94.77%, tương ứng cho phân loại nhị phân và đa lớp. Mô hình ra quyết định phát triển được đánh giá dựa trên tính hữu ích của nó trong việc giải quyết vấn đề ra quyết định, và độ chính xác trong việc đề xuất quyết định phù hợp khoảng 97.80%.
Từ khóa
#COVID-19 #quyết định tự động #mạng Bayesian #quy tắc kết hợp #phân loạiTài liệu tham khảo
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