최소 단어 이상 선택하여야 합니다.
최대 10 단어까지만 선택 가능합니다.
다음과 같은 기능을 한번의 로그인으로 사용 할 수 있습니다.
NTIS 바로가기Healthcare informatics research, v.29 no.3, 2023년, pp.228 - 238
Miranda, Eka , Adiarto, Suko , Bhatti, Faqir M. , Zakiyyah, Alfi Yusrotis , Aryuni, Mediana , Bernando, Charles
Objectives: The number of deaths from cardiovascular disease is projected to reach 23.3 million by 2030. As a contribution to preventing this phenomenon, this paper proposed a machine learning (ML) model to predict patients with arteriosclerotic heart disease (AHD). We also interpreted the predictio...
Vinciguerra, Mattia, Romiti, Silvia, Fattouch, Khalil, De Bellis, Antonio, Greco, Ernesto. Atherosclerosis as Pathogenetic Substrate for Sars-Cov2 Cytokine Storm. Journal of clinical medicine, vol.9, no.7, 2095-.
c2023
2019
Capotosto, Lidia, Massoni, Francesco, De Sio, Simone, Ricci, Serafino, Vitarelli, Antonio. Early Diagnosis of Cardiovascular Diseases in Workers: Role of Standard and Advanced Echocardiography. BioMed research international, vol.2018, 7354691-.
Muhammad, Yar, Tahir, Muhammad, Hayat, Maqsood, Chong, Kil To. Early and accurate detection and diagnosis of heart disease using intelligent computational model. Scientific reports, vol.10, no.1, 19747-.
Guo, Chao-Yu, Wu, Min-Yang, Cheng, Hao-Min. The Comprehensive Machine Learning Analytics for Heart Failure. International journal of environmental research and public health, vol.18, no.9, 4943-.
Karthick, K., Aruna, S. K., Samikannu, Ravi, Kuppusamy, Ramya, Teekaraman, Yuvaraja, Thelkar, Amruth Ramesh. Implementation of a Heart Disease Risk Prediction Model Using Machine Learning. Computational and mathematical methods in medicine : CMMM, vol.2022, 6517716-.
Chen, Zihan, Yang, Minhui, Wen, Yuhang, Jiang, Songyan, Liu, Wenjun, Huang, Hui. Prediction of atherosclerosis using machine learning based on operations research. Mathematical biosciences and engineering : MBE, vol.19, no.5, 4892-4910.
Park, Samel, Hong, Min, Lee, HwaMin, Cho, Nam-jun, Lee, Eun-Young, Lee, Won-Young, Rhee, Eun-Jung, Gil, Hyo-Wook. New Model for Predicting the Presence of Coronary Artery Calcification. Journal of clinical medicine, vol.10, no.3, 457-.
Fan, Jiaxin, Chen, Mengying, Luo, Jian, Yang, Shusen, Shi, Jinming, Yao, Qingling, Zhang, Xiaodong, Du, Shuang, Qu, Huiyang, Cheng, Yuxuan, Ma, Shuyin, Zhang, Meijuan, Xu, Xi, Wang, Qian, Zhan, Shuqin. The prediction of asymptomatic carotid atherosclerosis with electronic health records: a comparative study of six machine learning models. BMC medical informatics and decision making, vol.21, no.1, 115-.
Ward, Andrew, Sarraju, Ashish, Chung, Sukyung, Li, Jiang, Harrington, Robert, Heidenreich, Paul, Palaniappan, Latha, Scheinker, David, Rodriguez, Fatima. Machine learning and atherosclerotic cardiovascular disease risk prediction in a multi-ethnic population. npj digital medicine, vol.3, no.1, 125-.
Terrada, Oumaima, Cherradi, Bouchaib, Raihani, Abdelhadi, Bouattane, Omar. A novel medical diagnosis support system for predicting patients with atherosclerosis diseases. Informatics in medicine unlocked, vol.21, 100483-.
Frizzell, Jarrod D., Liang, Li, Schulte, Phillip J., Yancy, Clyde W., Heidenreich, Paul A., Hernandez, Adrian F., Bhatt, Deepak L., Fonarow, Gregg C., Laskey, Warren K.. Prediction of 30-Day All-Cause Readmissions in Patients Hospitalized for Heart Failure : Comparison of Machine Learning and Other Statistical Approaches. JAMA cardiology, vol.2, no.2, 204-.
Budholiya, Kartik, Shrivastava, Shailendra Kumar, Sharma, Vivek. An optimized XGBoost based diagnostic system for effective prediction of heart disease. Journal of King Saud University. Computer and information sciences, vol.34, no.7, 4514-4523.
A unified approach to interpreting model predictions Lundberg 4765 2017
Cho, Eunnuri, Chang, Tai-Woo, Hwang, Gyusun. Data Preprocessing Combination to Improve the Performance of Quality Classification in the Manufacturing Process. Electronics, vol.11, no.3, 477-.
Absar, Nurul, Das, Emon Kumar, Shoma, Shamsun Nahar, Khandaker, Mayeen Uddin, Miraz, Mahadi Hasan, Faruque, M. R. I., Tamam, Nissren, Sulieman, Abdelmoneim, Pathan, Refat Khan. The Efficacy of Machine-Learning-Supported Smart System for Heart Disease Prediction. Healthcare, vol.10, no.6, 1137-.
Prediction of heart disease and classifiers’ sensitivity analysis Almustafa 278 2020
Su, Xi, Xu, Yongyong, Tan, Zhijun, Wang, Xia, Yang, Peng, Su, Yani, Jiang, Yangyang, Qin, Sijia, Shang, Lei. Prediction for cardiovascular diseases based on laboratory data: An analysis of random forest model. Journal of clinical laboratory analysis, vol.34, no.9, e23421-.
Cao, Jiaoyu, Zhang, Lixiang, Ma, Likun, Zhou, Xiaojuan, Yang, Beibei, Wang, Wenjing. Study on the risk of coronary heart disease in middle-aged and young people based on machine learning methods: a retrospective cohort study. PeerJ, vol.10, e14078-.
Mahesh, T. R., Dhilip Kumar, V., Vinoth Kumar, V., Asghar, Junaid, Geman, Oana, Arulkumaran, G., Arun, N.. AdaBoost Ensemble Methods Using K-Fold Cross Validation for Survivability with the Early Detection of Heart Disease. Computational intelligence and neuroscience, vol.2022, 9005278-.
Alelyani, Salem. Detection and Evaluation of Machine Learning Bias. Applied sciences, vol.11, no.14, 6271-.
He, Shengnan, Qu, Long, He, Xi, Zhang, Dan, Xie, Ni. Comparative evaluation of 15-minute rapid diagnosis of ischemic heart disease by high-sensitivity quantification of cardiac biomarkers. Experimental and therapeutic medicine, vol.20, no.2, 1702-1708.
Understanding heart failure patients EHR clinical features via SHAP interpretation of tree-based machine learning model predictions Lu 813 2022
Futagami, Katsuya, Fukazawa, Yusuke, Kapoor, Nakul, Kito, Tomomi. Pairwise acquisition prediction with SHAP value interpretation. The journal of finance and data science, vol.7, 22-44.
Wang, Ke, Tian, Jing, Zheng, Chu, Yang, Hong, Ren, Jia, Liu, Yanling, Han, Qinghua, Zhang, Yanbo. Interpretable prediction of 3-year all-cause mortality in patients with heart failure caused by coronary heart disease based on machine learning and SHAP. Computers in biology and medicine, vol.137, 104813-.
Lee, Gyeongsil, Choi, Seulggie, Kim, Kyuwoong, Yun, Jae‐Moon, Son, Joung Sik, Jeong, Su‐Min, Kim, Sung Min, Park, Sang Min. Association of Hemoglobin Concentration and Its Change With Cardiovascular and All‐Cause Mortality. Journal of the American Heart Association : cardiovascular and cerebrovascular disease, vol.7, no.3, e007723-.
Goel, Harsh, Hirsch, Joshua R., Deswal, Anita, Hassan, Saamir A.. Anemia in Cardiovascular Disease: Marker of Disease Severity or Disease-modifying Therapeutic Target?. Current atherosclerosis reports, vol.23, no.10, 61-.
※ AI-Helper는 부적절한 답변을 할 수 있습니다.