Alawi Alqushaibi^1,2,*, Mohd Hilmi Hasan^1,2, Said Jadid Abdulkadir^1,2, Amgad Muneer^1,2, Mohammed Gamal^1,2, Qasem Al-Tashi³, Shakirah Mohd Taib^1,2, Hitham Alhussian^1,2

CMC-Computers, Materials & Continua, Vol.75, No.2, pp. 3223-3238, 2023, DOI:10.32604/cmc.2023.035655

Abstract Diabetes mellitus is a long-term condition characterized by hyperglycemia. It could lead to plenty of difficulties. According to rising morbidity in recent years, the world’s diabetic patients will exceed 642 million by 2040, implying that one out of every ten persons will be diabetic. There is no doubt that this startling figure requires immediate attention from industry and academia to promote innovation and growth in diabetes risk prediction to save individuals’ lives. Due to its rapid development, deep learning (DL) was used to predict numerous diseases. However, DL methods still suffer from their limited prediction performance due to the hyperparameters… More >

Nisha Mary¹, Bilal Khan¹, Abdullah A. Asiri², Fazal Muhammad^3,*, Salman Khan³, Samar Alqhtani⁴, Khlood M. Mehdar⁵, Hanan Talal Halwani⁴, Muhammad Irfan⁶, Khalaf A. Alshamrani²

CMC-Computers, Materials & Continua, Vol.73, No.3, pp. 6595-6616, 2022, DOI:10.32604/cmc.2022.032384

Abstract Heart disease prognosis (HDP) is a difficult undertaking that requires knowledge and expertise to predict early on. Heart failure is on the rise as a result of today’s lifestyle. The healthcare business generates a vast volume of patient records, which are challenging to manage manually. When it comes to data mining and machine learning, having a huge volume of data is crucial for getting meaningful information. Several methods for predicting HD have been used by researchers over the last few decades, but the fundamental concern remains the uncertainty factor in the output data, as well as the need to decrease… More >

Nisha Mary¹, Bilal Khan^1,*, Abdullah A Asiri², Fazal Muhammad³, Samar Alqhtani⁴, Khlood M Mehdar⁵, Hanan Talal Halwani⁴, Turki Aleyani⁴, Khalaf A Alshamrani²

Journal of Intelligent Medicine and Healthcare, Vol.1, No.1, pp. 11-31, 2022, DOI:10.32604/jimh.2022.030161

Abstract Prognosis of HD is a complex task that requires experience and expertise to predict in the early stage. Nowadays, heart failure is rising due to the inherent lifestyle. The healthcare industry generates dense records of patients, which cannot be managed manually. Such an amount of data is very significant in the field of data mining and machine learning when gathering valuable knowledge. During the last few decades, researchers have used different approaches for the prediction of HD, but still, the major problem is the uncertainty factor in the output data and also there is a need to reduce the error… More >

Ling Tan¹, Yun Tan², Jiaohua Qin², Hao Tang^1,*, Xuyu Xiang², Dongshu Xie¹, Neal N. Xiong³

CMC-Computers, Materials & Continua, Vol.69, No.2, pp. 2583-2598, 2021, DOI:10.32604/cmc.2021.017779

Abstract During the COVID-19 pandemic, the treatment of aortic dissection has faced additional challenges. The necessary medical resources are in serious shortage, and the preoperative waiting time has been significantly prolonged due to the requirement to test for COVID-19 infection. In this work, we focus on the risk prediction of aortic dissection surgery under the influence of the COVID-19 pandemic. A general scheme of medical data processing is proposed, which includes five modules, namely problem definition, data preprocessing, data mining, result analysis, and knowledge application. Based on effective data preprocessing, feature analysis and boosting trees, our proposed fusion decision model can… More >

Dejia Shi¹, Hanzhong Zheng^2,*

CMES-Computer Modeling in Engineering & Sciences, Vol.128, No.1, pp. 161-181, 2021, DOI:10.32604/cmes.2021.014917

Abstract ICU patients are vulnerable to medications, especially infusion medications, and the rate and dosage of infusion drugs may worsen the condition. The mortality prediction model can monitor the real-time response of patients to drug treatment, evaluate doctors’ treatment plans to avoid severe situations such as inverse Drug-Drug Interactions (DDI), and facilitate the timely intervention and adjustment of doctor’s treatment plan. The treatment process of patients usually has a time-sequence relation (which usually has the missing data problem) in patients’ treatment history. The state-of-the-art method to model such time-sequence is to use Recurrent Neural Network (RNN). However, sometimes, patients’ treatment can… More >

Yuli Y. Kim^1,2, Leah A. Goldberg², Katherine Awh², Tanmay Bhamare^1,2, David Drajpuch², Adi Hirshberg³, Sara L. Partington^1,2, Rachel Rogers⁴, Emily Ruckdeschel^1,2, Lynda Tobin¹, Morgan Venuti², Lisa D. Levine³

Congenital Heart Disease, Vol.14, No.3, pp. 470-478, 2019, DOI:10.1111/chd.12750

Abstract Objective: To assess performance of risk stratification schemes in predicting adverse cardiac outcomes in pregnant women with congenital heart disease (CHD) and to compare these schemes to clinical factors alone.
Design: Single‐center retrospective study.
Setting: Tertiary care academic hospital.
Patients: Women ≥18 years with International Classification of Diseases, Ninth Revision, Clinical Modification codes indicating CHD who delivered between 1998 and 2014. CARPREG I and ZAHARA risk scores and modified World Health Organization (WHO) criteria were applied to each woman.
Outcome Measures: The primary outcome was defined by ≥1 of the following: arrhyth‐ mia, heart failure/pulmonary edema, transient ischemic attack, stroke,… More >