S. Kusuma^*, Dr. Jothi K. R

Computer Systems Science and Engineering, Vol.41, No.3, pp. 1273-1289, 2022, DOI:10.32604/csse.2022.021741 - 10 November 2021

Abstract One of the severe health problems and the most common types of heart disease (HD) is Coronary heart disease (CHD). Due to the lack of a healthy lifestyle, HD would cause frequent mortality worldwide. If the heart attack occurs without any symptoms, it cannot be cured by an intelligent detection system. An effective diagnosis and detection of CHD should prevent human casualties. Moreover, intelligent systems employ clinical-based decision support approaches to assist physicians in providing another option for diagnosing and detecting HD. This paper aims to introduce a heart disease prediction model including phases like… More >

Mohd Zubir Suboh^1,2, Nazrul Anuar Nayan^1,3,*, Noraidatulakma Abdullah^4,5, Nurul Ain Mhd Yusof⁴, Mariatul Akma Hamid⁴, Azwa Shawani Kamalul Arinfin⁴, Syakila Mohd Abd Daud⁴, Mohd Arman Kamaruddin⁴, Rosmina Jaafar¹, Rahman Jamal⁴

CMC-Computers, Materials & Continua, Vol.71, No.1, pp. 1111-1132, 2022, DOI:10.32604/cmc.2022.022123 - 03 November 2021

Abstract A comprehensive study was conducted to differentiate cardiovascular disease (CVD) subjects from non-CVD subjects using short recording electrocardiogram (ECG) of 244 Malaysian adults in The Malaysian Cohort project. An automated peak detection algorithm to detect nine fiducial points of electrocardiogram (ECG) was developed. Forty-eight features were extracted in both time and frequency domains, including statistical features obtained from heart rate variability and Poincare plot analysis. These include five new features derived from spectrum counts of five different frequency ranges. Feature selection was then made based on p-value and correlation matrix. Selected features were used as… More >

Shobhit Verma¹ , Nonita Sharma¹ , Aman Singh² , Abdullah Alharbi³ , Wael Alosaimi³ , Hashem Alyami⁴, Deepali Gupta⁵, Nitin Goyal^{5 ,*}

CMC-Computers, Materials & Continua, Vol.70, No.3, pp. 6041-6055, 2022, DOI:10.32604/cmc.2022.021747 - 11 October 2021

Abstract This research work proposes a new stack-based generalization ensemble model to forecast the number of incidences of conjunctivitis disease. In addition to forecasting the occurrences of conjunctivitis incidences, the proposed model also improves performance by using the ensemble model. Weekly rate of acute Conjunctivitis per 1000 for Hong Kong is collected for the duration of the first week of January 2010 to the last week of December 2019. Pre-processing techniques such as imputation of missing values and logarithmic transformation are applied to pre-process the data sets. A stacked generalization ensemble model based on Auto-ARIMA (Autoregressive… More >

Vinothini Arumugam^*, S. Baghavathi Priya

Intelligent Automation & Soft Computing, Vol.31, No.2, pp. 947-959, 2022, DOI:10.32604/iasc.2022.018654 - 22 September 2021

Abstract Nowadays, Chronic Kidney Disease (CKD) is one of the vigorous public health diseases. Hence, early detection of the disease may reduce the severity of its consequences. Besides, medical databases of any disease diagnosis may be collected from the blood test, urine test, and patient history. Nevertheless, medical information retrieved from various sources is diverse. Therefore, it is unadaptable to evaluate numerical and nominal features using the same feature selection algorithm, which may lead to fallacious analysis. Applying machine learning techniques over the medical database is a common way to help feature identification for CKD prediction.… More >

Mohammad Tabrez Quasim¹, Saad Alhuwaimel^2,*, Asadullah Shaikh³, Yousef Asiri³, Khairan Rajab³, Rihem Farkh^4,5, Khaled Al Jaloud⁴

CMC-Computers, Materials & Continua, Vol.69, No.3, pp. 4169-4181, 2021, DOI:10.32604/cmc.2021.015984 - 24 August 2021

Abstract Heart disease is the leading cause of death worldwide. Predicting heart disease is challenging because it requires substantial experience and knowledge. Several research studies have found that the diagnostic accuracy of heart disease is low. The coronary heart disorder determines the state that influences the heart valves, causing heart disease. Two indications of coronary heart disorder are strep throat with a red persistent skin rash, and a sore throat covered by tonsils or strep throat. This work focuses on a hybrid machine learning algorithm that helps predict heart attacks and arterial stiffness. At first, we More >

Shuxuan Xie, Zengchen Yu, Zhihan Lv^*

CMES-Computer Modeling in Engineering & Sciences, Vol.128, No.2, pp. 489-522, 2021, DOI:10.32604/cmes.2021.016728 - 22 July 2021

Abstract In recent years, the development of artificial intelligence (AI) and the gradual beginning of AI’s research in the medical field have allowed people to see the excellent prospects of the integration of AI and healthcare. Among them, the hot deep learning field has shown greater potential in applications such as disease prediction and drug response prediction. From the initial logistic regression model to the machine learning model, and then to the deep learning model today, the accuracy of medical disease prediction has been continuously improved, and the performance in all aspects has also been significantly More >

Munir Ahmad¹, Majed Alfayad², Shabib Aftab^1,3, Muhammad Adnan Khan^4,*, Areej Fatima⁵, Bilal Shoaib⁶, Mohammad Sh. Daoud⁷, Nouh Sabri Elmitwally^2,8

CMC-Computers, Materials & Continua, Vol.69, No.2, pp. 2717-2731, 2021, DOI:10.32604/cmc.2021.019013 - 21 July 2021

Abstract Heart disease, which is also known as cardiovascular disease, includes various conditions that affect the heart and has been considered a major cause of death over the past decades. Accurate and timely detection of heart disease is the single key factor for appropriate investigation, treatment, and prescription of medication. Emerging technologies such as fog, cloud, and mobile computing provide substantial support for the diagnosis and prediction of fatal diseases such as diabetes, cancer, and cardiovascular disease. Cloud computing provides a cost-efficient infrastructure for data processing, storage, and retrieval, with much of the extant research recommending More >

Pushpendra Kumar^1,2,*, Ramjeevan Singh Thakur³

CMC-Computers, Materials & Continua, Vol.68, No.3, pp. 3513-3529, 2021, DOI:10.32604/cmc.2021.016957 - 06 May 2021

Abstract The aim of this research is to develop a mechanism to help medical practitioners predict and diagnose liver disease. Several systems have been proposed to help medical experts by diminishing error and increasing accuracy in diagnosing and predicting diseases. Among many existing methods, a few have considered the class imbalance issues of liver disorder datasets. As all the samples of liver disorder datasets are not useful, they do not contribute to learning about classifiers. A few samples might be redundant, which can increase the computational cost and affect the performance of the classifier. In this… More >

Muhammad Waqas Nadeem^1,2, Hock Guan Goh^1,*, Muhammad Adnan Khan³, Muzammil Hussain⁴, Muhammad Faheem Mushtaq⁵, Vasaki a/p Ponnusamy¹

CMC-Computers, Materials & Continua, Vol.67, No.2, pp. 2481-2496, 2021, DOI:10.32604/cmc.2021.014649 - 05 February 2021

Abstract The contemporary evolution in healthcare technologies plays a considerable and significant role to improve medical services and save human lives. Heart disease or cardiovascular disease is the most fatal and complex disease which it is hardly to be detected through our naked eyes, as numerous people have been suffering from this disease globally. Heart attacks occur when the ranges of vital signs such as blood pressure, pulse rate, and body temperature exceed their normal values. The efficient diagnosis of heart diseases could play a substantial role in the field of cardiology, while diagnostic time could… More >

Bin Yang^1,*, Lingyun Xiang², Xianyi Chen³, Wenjing Jia⁴

CMC-Computers, Materials & Continua, Vol.67, No.1, pp. 951-964, 2021, DOI:10.32604/cmc.2021.014839 - 12 January 2021

Abstract Many chronic disease prediction methods have been proposed to predict or evaluate diabetes through artificial neural network. However, due to the complexity of the human body, there are still many challenges to face in that process. One of them is how to make the neural network prediction model continuously adapt and learn disease data of different patients, online. This paper presents a novel chronic disease prediction system based on an incremental deep neural network. The propensity of users suffering from chronic diseases can continuously be evaluated in an incremental manner. With time, the system can… More >