Heart Disease Diagnosis System Using Fuzzy Logic

Kahtan, Hasan; Zamli, Kamal Z.; Fatthi, Wan Nor Ashikin Wan Ahmad; Abdullah, Azma binti; Abdulleteef, Mansoor; Kamarulzaman, Noor Shahaiyusniezam

doi:10.1145/3185089.3185118

Cited by 17 publications

(8 citation statements)

References 9 publications

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“…6) Increased efficiency and reduced costs: The use of AI technologies can help streamline healthcare processes and reduce costs. By automating the collection and analysis of health-related data, these systems can reduce the need for manual data entry and analysis, saving time and resources [3], [26], [70]- [72].…”

Section: B Opportunitiesmentioning

confidence: 99%

Balancing Technological Advances with User Needs: User-centered Principles for AI-Driven Smart City Healthcare Monitoring

Hassan¹,

Sulaiman²,

Abdulgabber³

et al. 2023

IJACSA

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In recent years, the integration of artificial intelligence (AI) technologies has greatly benefited smart city healthcare, meeting the growing demand for affordable, efficient, and real-time healthcare services. Patient monitoring is one area where artificial intelligence has shown great promise. Improved health outcomes have been made possible by the advancement of AI-based monitoring systems, which enable more personalized and continuous patient monitoring. However, to fully maximize the benefits of these systems, a user-centered approach is essential, which prioritizes patients' needs and experiences while ensuring their privacy and autonomy are respected. This study focuses on the application of user-centered design principles in the development and deployment of AI-driven monitoring systems in smart city healthcare. Addressing the challenges and opportunities of AI-driven monitoring systems, the article considers issues such as privacy and security concerns, data accuracy, and user acceptance. Finally, some possible future directions to the challenges are suggested. A user-centered approach to AI monitoring systems is recommended for healthcare providers to enhance patient experience in smart city healthcare.

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Section: B Opportunitiesmentioning

confidence: 99%

Balancing Technological Advances with User Needs: User-centered Principles for AI-Driven Smart City Healthcare Monitoring

Hassan¹,

Sulaiman²,

Abdulgabber³

et al. 2023

IJACSA

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“…Likewise, for scoring the symptoms statistical test chi-square, Fisher's exact test and risk score tree are used. BP algorithm is used to extract attributes and syndromes in recent past 2018 [17]- [21].…”

Section: Review Of Relevant Workmentioning

confidence: 99%

Enhanced Accuracy of Heart Disease Prediction using Machine Learning and Recurrent Neural Networks Ensemble Majority Voting Method

Javid¹,

Alsaedi²,

Ghazali³

2020

IJACSA

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To solve many problems in data science, Machine Learning (ML) techniques implicates artificial intelligence which are commonly used. The major utilization of ML is to predict the conclusion established on the extant data. Using an established dataset machine determine emulate and spread them to an unfamiliar data sets to anticipate the conclusion. A few classification algorithm's accuracy prediction is satisfactory, although other perform limited accuracy. Different ML and Deep Learning (DL) networks established on ANN have been extensively recommended for the disclosure of heart disease in antecedent researches. In this paper, we used UCI Heart Disease dataset to test ML techniques along with conventional methods (i.e. random forest, support vector machine, K-nearest neighbor), as well as deep learning models (i.e. long short-term-memory and gated-recurrent unit neural networks). To improve the accuracy of weak algorithms we explore voting based model by combining multiple classifiers. A provisional cogent approach was used to regulate how the ensemble technique can be enforced to improve an accuracy in the heart disease prediction. The strength of the proposed ensemble approach such as voting based model is compelling in improving the prognosis accuracy of anemic classifiers and established adequate achievement in analyze risk of heart disease. A superlative increase of 2.1% accuracy for anemic classifiers was attained with the help of an ensemble voting based model.

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“…It is feasible that data-driven fuzzy logic might assist clear up the ambiguity in demand infrastructure [ 21 ]. A number of key approaches and processes have enabled patient diagnosis of disease and pain [ 14 , 22 – 25 ]. The formal rules classification is pretty precise when it comes to prognostic price and diagnostic accuracy [ 26 , 27 ].…”

Section: Introductionmentioning

confidence: 99%

Diagnosis of Chronic Ischemic Heart Disease Using Machine Learning Techniques

Shehzadi

Hassan

Rizwan

et al. 2022

Computational Intelligence and Neuroscience

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Ischemic heart disease (IHD) causes discomfort or irritation in the chest. According to the World Health Organization, coronary heart disease is the major cause of mortality in Pakistan. Accurate model with the highest precision is necessary to avoid fatalities. Previously several models are tried with different attributes to enhance the detection accuracy but failed to do so. In this research study, an artificial approach to categorize the current stage of heart disease is carried out. Our model predicts a precise diagnosis of chronic diseases. The system is trained using a training dataset and then tested using a test dataset. Machine learning methods such as LR, NB, and RF are applied to forecast the development of a disease. Experimental outcomes of this research study have proven that our strategy has excelled other procedures with maximum accuracy of 99 percent for RF, 97 percent for NB, and 98 percent for LR. With such high accuracy, the number of deaths per year of ischemic heart disease will be slightly decreased.

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Heart Disease Diagnosis System Using Fuzzy Logic

Cited by 17 publications

References 9 publications

Balancing Technological Advances with User Needs: User-centered Principles for AI-Driven Smart City Healthcare Monitoring

Balancing Technological Advances with User Needs: User-centered Principles for AI-Driven Smart City Healthcare Monitoring

Enhanced Accuracy of Heart Disease Prediction using Machine Learning and Recurrent Neural Networks Ensemble Majority Voting Method

Diagnosis of Chronic Ischemic Heart Disease Using Machine Learning Techniques

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