A systematic review on Data Mining Application in Parkinson's disease

Srivastava, Adesh Kumar; Jeberson, Klinsega; Jeberson, Wilson

doi:10.1016/j.neuri.2022.100064

Cited by 7 publications

(3 citation statements)

References 50 publications

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“…Srivastava ve diğerleri, 2004-2020 yılları arasında Parkinson hastalığının tanı ve tedavi yöntemlerine ilişkin kullanılan 159 veri madenciliği çalışmasını incelemiştir [38]. Parkinson hastalığına ilişkin erken tanı tahmini ve Parkinson hastalığı tedavi yönetimi uygulamalarını değerlendirmiştir [38].…”

Section: Disiplinlerarası Yen Araş Der 3(1) 23-30 2023 Koçak Ve Ergünunclassified

Sağlıkta veri kalitesi ve veri madenciliği uygulamaları

KOÇAK

ERGÜN

2023

Disiplinlerarası Yenilik Araştırmaları Dergisi

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Veri günümüzde çok sık karşılaşılan bir terimdir. Verinin doğru kullanımı doğru değerlendirmeyi sağlar. Bu da kaynakların verimli kullanımını, verilen hizmetin kalitesinin artmasını sağlamaktadır. Verinin en çok toplandığı alanların başında sağlık sektörü gelmektedir. Sağlık hizmet sunumunun maddi ve manevi yükü ağırdır. Bu hizmetin en iyi şekilde verilmesi, kaynakların doğru kullanılması ile yakın ilişkilidir. Sağlık verilerinden anlamlı sonuçların çıkarılarak hekimlere, hemşirelere ve sağlık yöneticileri gibi sağlık sektörü çalışanlarına yön gösterecek bilgilerin sağlanması sağlık verilerinin büyüklüğü düşünüldüğünde ancak veri madenciliği metotları ile mümkündür. Sağlık sektörünün insan hayatını direkt etkileyen bir doğası olması sebebi ile sağlıkta kullanılan verilerin kalitesinin en üst düzeyde olması beklenmektedir. Bu çalışmada veri kalitesini ve veri madenciliğini bütüncül olarak ele almıştır. Uygulama örnekleri aracılığıyla veri madenciliği ile sağlık sektöründe ne tür çalışmalar yapılabileceğine dair genel bir bakış açısı sağlanmıştır.

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Section: Disiplinlerarası Yen Araş Der 3(1) 23-30 2023 Koçak Ve Ergünunclassified

Sağlıkta veri kalitesi ve veri madenciliği uygulamaları

KOÇAK

ERGÜN

2023

Disiplinlerarası Yenilik Araştırmaları Dergisi

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“…Recent reviews have indicated that data mining techniques can be utilized as a tool contributing to the detection of various illnesses [16][17][18][19][20][21]. A systematic review entitled "an overview of heart disease prediction" examined predictive data mining for medical diagnosis in 2011 [7].…”

Section: Introductionmentioning

confidence: 99%

Diagnosis of Heart Disease Using Data Mining Techniques: A Systematic Review of Influential Factors and Outcomes

Gozali,

Hajesmaeel-Gohari,

Khademvatani

et al. 2024

Front Health Inform

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Introduction: Heart disease is a major public health concern with millions of reported deaths annually. Data mining techniques have received attention in recent years as a tool aiding diagnosis and prediction of heart disease cases. This systematic review examines the application of data mining methods to cardiac disease diagnosis in order to identify specific types of heart-related disease that are diagnosed using data mining techniques as well as the most successful data mining methods.Material and Methods: This study involved a systematic review of IEEE, Science Direct, Google Scholar, Web of Science, Scopus and MEDLINE databases from 2008 until April 2023. Inclusion criteria were original papers that used data mining methods for heart disease diagnosis. Non-English papers, those without full text, studies conducted on animals, and other types of papers (conference abstracts and letters) were excluded from the study. All the retrieved references were then assessed by title and abstract according to PRISMA, after which full texts of relevant articles were analyzed. The final sample comprised of 47 articles.Results: Various classification methods have been utilized to diagnose heart-related disease using different mining tools, with genetic neural network data mining method having the highest accuracy among the studied techniques. Results show that predicting cardiac disease is the most commonly performed task. The demographic, bio-clinical, personal and exercise-related attributes, as well as other features used for classification were identified. The findings suggest that data mining methods hold great potential for detecting and preventing heart disease on both individual and population scales.Conclusion: The study findings have implications for the prevention and treatment of cardiac disease, especially in high-risk individuals. Data mining methods can be widely applied to detect and prevent heart disease on a population scale, as well as supporting decisions for the most suitable treatment for individual patients to prevent death and reduce treatment costs.

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“…To address these difficulties and refine the early detection of PD, different neuroimaging techniques (such as magnetic resonance imaging (MRI), computed tomography (CT) and positron emission tomography (PET)) and deep learning-based analysis methods have been developed [5]. Data mining techniques [6] have taken a significant role in the early diagnosis and prognosis of many health diseases, including Parkinson's Disease. Data mining is the process of extracting previously unknown knowledge from a large collection of data.…”

Section: Introductionmentioning

confidence: 99%

Untitled

2022

IJDKP

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Parkinson's Disease (PD) is a progressive neurodegenerative disorder that causes uncontrollable movements and difficulty with balance and coordination. It is highly important for early detection of Parkinson's Disease for patients to receive proper treatment. This paper aims to present a preliminary data mining procedure that help Parkinson's Disease patients slow down their progression of the disease while helping early detection of the disease. For early non-invasive treatment, our research first analyses the early symptoms of Parkinson's Disease, designs/selects a proper demo video, let the user follow the demo to exercise and upload his exercise video to our deep learning APP: LaBelle. LaBelle utilizing MediaPipe Pose to identify, analyze, and store data about the poses and movements of both demo and the user, calculates the angles created between different joints and major body parts. LaBelle's AI model uses a K-means clustering algorithm to create a group of clusters for both demo and the user dataset. Using the two sets of clusters, LaBelle identifies the key frames in the user video and searches the demo cluster set for a matching set of properties and frames. It evaluates the differences between the paired frames and produces a final score as well as feedback on the poses that need improving. Meanwhile, if the user is willing to donate their exercise data, he can simply input his age, whether he is a PD patient (maybe for how long) anonymously. Then his data can be stored into our customized dataset, used in data mining for Parkinson's Disease prediction, which involves building/training our deep learning CNN model and help early detection of Parkinson's Disease.

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A systematic review on Data Mining Application in Parkinson's disease

Cited by 7 publications

References 50 publications

Sağlıkta veri kalitesi ve veri madenciliği uygulamaları

Sağlıkta veri kalitesi ve veri madenciliği uygulamaları

Diagnosis of Heart Disease Using Data Mining Techniques: A Systematic Review of Influential Factors and Outcomes

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