An Improved Machine Learning Technique with Effective Heart Disease Prediction System

Quasim, Mohammad Tabrez; Alhuwaimel, Saad; Shaikh, Asadullah; Asiri, Yousef; Rajab, Khairan; Farkh, Rihem; jaloud, Khaled Al

doi:10.32604/cmc.2021.015984

Cited by 3 publications

(3 citation statements)

References 15 publications

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“…According to [35] peer influence plays a key role in determining learners' behavioral intentions to adopted mobile technologies. Table 1 presents a list of the reviewed studies [37].…”

Section: Resultsmentioning

confidence: 99%

Technology Based Model of a Mobile Knowledge as a Service to Facilitate Education Community

Badawood

AlBadri

2021

Int. J. Interact. Mob. Technol.

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Technological enhancements as well as the demand of students to access learning information on time and quickly has resulted to the development of e-learning across the world. Mobile learning has been adopted by most learning institution as a mobile technology that allows learners to access learning materials and share information among themselves and respective educators easily and quickly. This article discusses the intention of users in learning environment adopting m-learning, their perceptions as well as factors that hinder implementation of m-learning in the gulf region. Effective mobile technology adoption also enhances knowledge management through mobile applications that allow information capture, storage, retrieval and sharing. This study uses systematic literature review to collect information from post 2017 studies previous conducted by other researchers. Articles were searched through highly ranked databases from which 657 journals were identified. After the screening and eligibility assessment, 24 journals were retrieved. The back and forward search retrieved 4 more journals bringing the total to 28 journals that were included in the study. Based on this information, a conceptual model is developed to help assess the m-learning perceptions, adoption intentions and factors influencing its adoption among learning institutions in the gulf region. This model is built based on the Theory of Planned Behavior and Unified Theory of Acceptance and Use of Technology. Based on the developed model, main constructs such as performance expectancy, effort expectancy and social influence are greatly impacted by other factors like learner’s creativity and mobility.

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“…According to [35] peer influence plays a key role in determining learners' behavioral intentions to adopted mobile technologies. Table 1 presents a list of the reviewed studies [37].…”

Section: Resultsmentioning

confidence: 99%

Technology Based Model of a Mobile Knowledge as a Service to Facilitate Education Community

Badawood

AlBadri

2021

Int. J. Interact. Mob. Technol.

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“…Then converts the PDFs to a CSV file that deals with the ML code. First, we upload a folder that contains the CSV files to train it, Then reading the data files that we uploaded and saving them into new variables, then Put the uploaded files that we saved into variables to loop over it, after that cleaning the dataset by removing the unnecessary data such as numbers, and punctuation and remove it from the dataset, Next splitting the dataset by taking every file in our dataset and split it into two columns the X column have the cleaned data and the Y column the manually classify [24][25].…”

Section: Methodsmentioning

confidence: 99%

Analyzing Graduation Project Ideas by using Machine Learning

Alharbi

Alshaya

Alsheail

et al. 2021

Int. J. Interact. Mob. Technol.

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The graduation projects (GP) are important because it reflects the academic profile and achievement of the students. For many years’ graduation projects are done by the information technology department students. Most of these projects have great value, and some were published in scientific journals and international conferences. However, these projects are stored in an archive room haphazardly and there is a very small part of it is a set of electronic PDF files stored on hard disk, which wastes time and effort and cannot benefit from it. However, there is no system to classify and store these projects in a good way that can benefit from them. In this paper, we reviewed some of the best machine learning algorithms to classify text “graduation projects”, support vector machine (SVM) algorithm, logistic regression (LR) algorithm, random forest (RF) algorithm, which can deal with an extremely small amount of dataset after comparing these algorithms based on accuracy. We choose the SVM algorithm to classify the projects. Besides, we will mention how to deal with a super small dataset and solve this problem.

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“…In addition to having one of the maximum rates of hypertension in the world (44%), African Americans are two to three times more likely than non-Hispanic Whites to have a stroke. Previous studies on the CVD risk of African Americans focused on residents of the US as a whole or specific US states [4], [5]. These findings are constrained because they can unintentionally ignore the distinctive traits of different geographical populations and cultural variations within communities.…”

Section: Cardiovascular Diseasesmentioning

confidence: 99%

Early Detection of Heart Disease Using Machine Learning Approach

Zabeeulla¹,

Sharma²,

Anand³

2023

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Heart attack is one of the leading causes of morbidity in the worldwide population. Cardiovascular disease is one of the major diseases involved in clinical data analysis or one of the most important part for forecasting. Early detection of cardiovascular diseases can help to reduce high-risk condition for heart patients to make individual decisions for their lifestyle adjustments, mitigating the challenges. Early detection of heart disease has been explored in this study using a machine-learning approach. Additionally, we used sampling strategies to deal with disparate datasets. The overall risk is estimated using a variety of machine-learning techniques. On Kaggle, the Heart Disease dataset is accessible and open for all. In present study testing set used this dataset. The ultimate objective is to determine whether the patient has a “10-year risk of developing coronary heart disease” (CHD). The dataset contained thirteen features that provided patient data, and the authors used machine learning algorithms to diagnose cardiac problems with 98.8% accuracy.

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An Improved Machine Learning Technique with Effective Heart Disease Prediction System

Cited by 3 publications

References 15 publications

Technology Based Model of a Mobile Knowledge as a Service to Facilitate Education Community

Technology Based Model of a Mobile Knowledge as a Service to Facilitate Education Community

Analyzing Graduation Project Ideas by using Machine Learning

Early Detection of Heart Disease Using Machine Learning Approach

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