Predicting Heart Ailment in Patients with Varying number of Features using Data Mining Techniques

Mary, T R Stella; Sebastian, Shoney

doi:10.11591/ijece.v9i4.pp2675-2681

Cited by 15 publications

(14 citation statements)

References 7 publications

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“…Many research works have shown that Machine Learning algorithms are employed as technologies in Data Mining, which have played an important role in the application of classification, analysis, prediction and pattern recognition on many health issues [33][34][35][36]. Especially in diagnosis of disease, such as the cancer, heart, diabetes and Alzheimer disease, etc.…”

Section: Literature Reviewmentioning

confidence: 99%

Knowledge Extraction Methods as a Measurement Tool of Depression Discovery in Saudi Society

Al-Hagery¹,

Alfaozan²,

Alghofaily³

et al. 2020

IJITCS

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Depression is a widespread and serious phenomenon in public health in all societies. In Saudi society, depression is one of the diseases that the community is may refuse to disclose it. There are no studies have analyzed this disease within the Saudi community. The main research objective is to discover the depression level of Saudi People's. In addition to analyzing the age group and the most gender type affected by the depression in this society. The data collected from social media achieved indirectly without any communication with patients as a sample from this society people. It analyzed using Machine Learning algorithms that give accurate results for this disease. Three classification models have been established to diagnose this disease and the findings of this study presented that the depression levels include five classes and the most affected age group in depression was in the age group from 20-26 years. The results show that young Saudi women are more likely to be depressed. The obtained results are very important to the medical field. Researchers and people working in this field can get benefits out of this research. Especially those who want to understand the depression disease in Saudi society and searching for real solutions to overcome this problem.

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Section: Literature Reviewmentioning

confidence: 99%

Knowledge Extraction Methods as a Measurement Tool of Depression Discovery in Saudi Society

Al-Hagery¹,

Alfaozan²,

Alghofaily³

et al. 2020

IJITCS

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“…In recent years, the amount of data in the healthcare centers is large. Machine learning algorithms are widely applied to the larger medical datasets to discover interesting pattern and knowledge from the dataset and automate disease diagnosis with predictive model [4][5][6][7]. One of the approach for discovering important pattern from a medical dataset is exploring the relationships among dataset features.…”

Section: Introductionmentioning

confidence: 99%

Correlation Analysis for Determining Effective Data in Machine Learning: Detection of Heart Failure

Assegie

Sushma

Bhavya³

et al. 2021

SN COMPUT. SCI.

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Heart disease is one of the causes for death throughout the world. Heart disease cannot be easily identified by the medical experts and practitioners as the detection of heart disease requires expertise and experience. Hence, developing better performing models for heart disease detection using machine-learning algorithms is crucial for detecting heart disease in an early stage. However, employing machine learning algorithm involves determining the relationship between the heart failure dataset features. In this study, correlation analysis is employed to identify the relationship among the heart failure dataset features and a predictive model for heart failure detection is developed with K-nearest neighbor (KNN). Pearson correlation is employed to identify the relationship between the features in the heart failure dataset and the effect of strong correlation to the target feature on the performance of K-nearest neighbor (KNN) model is analyzed. The experimental result shows that highly correlated feature significantly affected the performance of K-nearest neighbor (KNN) for heart failure detection. Finally, the performance of KNN is evaluated and result reveals that the model has acceptable level of performance with highest accuracy of 97.07% on heart failure prediction.

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“…The cranial vessels for smell and vision originate in the cerebrum. Therefore, good blood circulation in the vessels Int J Artif Intell ISSN: 2252-8938 Brain stroke computed tomography images analysis using image processing: A review (Nur Hasanah Ali) 1049 is important to avoid neuronal damage and subsequent neurological deficits that will lead to cell death [3], [4].…”

Section: Introductionmentioning

confidence: 99%

Brain stroke computed tomography images analysis using image processing: A Review

Ali

Abdullah

Saad

et al. 2021

IJ-AI

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<span lang="EN-US">Stroke is the second-leading cause of death globally; therefore, it needs immediate treatment to prevent the brain from damage. Neuroimaging technique for stroke detection such as computed tomography (CT) has been widely used for emergency setting that can provide precise information on an obvious difference between white and gray matter. CT is the comprehensively utilized medical imaging technology for bone, soft tissue, and blood vessels imaging. A fully automatic segmentation became a significant contribution to help neuroradiologists achieve fast and accurate interpretation based on the region of interest (ROI). This review paper aims to identify, critically appraise, and summarize the evidence of the relevant studies needed by researchers. Systematic literature review (SLR) is the most efficient way to obtain reliable and valid conclusions as well as to reduce mistakes. Throughout the entire review process, it has been observed that the segmentation techniques such as fuzzy C-mean, thresholding, region growing, k-means, and watershed segmentation techniques were regularly used by researchers to segment CT scan images. This review is also impactful in identifying the best automated segmentation technique to evaluate brain stroke and is expected to contribute new information in the area of stroke research.</span>

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Predicting Heart Ailment in Patients with Varying number of Features using Data Mining Techniques

Cited by 15 publications

References 7 publications

Knowledge Extraction Methods as a Measurement Tool of Depression Discovery in Saudi Society

Knowledge Extraction Methods as a Measurement Tool of Depression Discovery in Saudi Society

Correlation Analysis for Determining Effective Data in Machine Learning: Detection of Heart Failure

Brain stroke computed tomography images analysis using image processing: A Review

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