Anomaly Detection in Heart Disease Using a Density-Based Unsupervised Approach

Nanehkaran, Yaser Ahangari; Licai, Zhu; Chen, Junde; Cemiloglu, Ahmed; Shengnan, Zhao; Navaei, Yahya Dorostkar; Aghbolagh, Mohsen Abdollahzadeh

doi:10.1155/2022/6913043

Cited by 16 publications

(8 citation statements)

References 18 publications

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“…Anomaly detection presents a promising approach in disease detection. Previous studies have explored the application of density-based anomaly detection algorithms to health data including heart disease, diabetes, and hepatitis [ 17 , 18 ].…”

Section: Introductionmentioning

confidence: 99%

Early warning systems for malaria outbreaks in Thailand: an anomaly detection approach

Srimokla,

Pan-Ngum,

Khamsiriwatchara

et al. 2024

Malar J

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Background Malaria continues to pose a significant health threat. Rapid identification of malaria infections and the deployment of active surveillance tools are crucial for achieving malaria elimination in regions where malaria is endemic, such as certain areas of Thailand. In this study, an anomaly detection system is introduced as an early warning mechanism for potential malaria outbreaks in countries like Thailand. Methods Unsupervised clustering-based, and time series-based anomaly detection algorithms are developed and compared to identify abnormal malaria activity in Thailand. Additionally, a user interface tailored for anomaly detection is designed, enabling the Thai malaria surveillance team to utilize these algorithms and visualize regions exhibiting unusual malaria patterns. Results Nine distinct anomaly detection algorithms we developed. Their efficacy in pinpointing verified outbreaks was assessed using malaria case data from Thailand spanning 2012 to 2022. The historical average threshold-based anomaly detection method triggered three times fewer alerts, while correctly identifying the same number of verified outbreaks when compared to the current method used in Thailand. A limitation of this analysis is the small number of verified outbreaks; further consultation with the Division of Vector Borne Disease could help identify more verified outbreaks. The developed dashboard, designed specifically for anomaly detection, allows disease surveillance professionals to easily identify and visualize unusual malaria activity at a provincial level across Thailand. Conclusion An enhanced early warning system is proposed to bolster malaria elimination efforts for countries with a similar malaria profile to Thailand. The developed anomaly detection algorithms, after thorough comparison, have been optimized for integration with the current malaria surveillance infrastructure. An anomaly detection dashboard for Thailand is built and supports early detection of abnormal malaria activity. In summary, the proposed early warning system enhances the identification process for provinces at risk of outbreaks and offers easy integration with Thailand’s established malaria surveillance framework.

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Section: Introductionmentioning

confidence: 99%

Early warning systems for malaria outbreaks in Thailand: an anomaly detection approach

Srimokla,

Pan-Ngum,

Khamsiriwatchara

et al. 2024

Malar J

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“…It is crucial to diagnose heart ailments with precision and timeliness by taking into account a patient’s medical history and lifestyle. This approach enables accurate prognosis and the implementation of preventive measures to manage or eradicate these potentially fatal illnesses [ 1 ].…”

Section: Introductionmentioning

confidence: 99%

Hyperparameter optimization for cardiovascular disease data-driven prognostic system

Saputra,

Lawrencya,

Saini

et al. 2023

Vis. Comput. Ind. Biomed. Art

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Prediction and diagnosis of cardiovascular diseases (CVDs) based, among other things, on medical examinations and patient symptoms are the biggest challenges in medicine. About 17.9 million people die from CVDs annually, accounting for 31% of all deaths worldwide. With a timely prognosis and thorough consideration of the patient’s medical history and lifestyle, it is possible to predict CVDs and take preventive measures to eliminate or control this life-threatening disease. In this study, we used various patient datasets from a major hospital in the United States as prognostic factors for CVD. The data was obtained by monitoring a total of 918 patients whose criteria for adults were 28-77 years old. In this study, we present a data mining modeling approach to analyze the performance, classification accuracy and number of clusters on Cardiovascular Disease Prognostic datasets in unsupervised machine learning (ML) using the Orange data mining software. Various techniques are then used to classify the model parameters, such as k-nearest neighbors, support vector machine, random forest, artificial neural network (ANN), naïve bayes, logistic regression, stochastic gradient descent (SGD), and AdaBoost. To determine the number of clusters, various unsupervised ML clustering methods were used, such as k-means, hierarchical, and density-based spatial clustering of applications with noise clustering. The results showed that the best model performance analysis and classification accuracy were SGD and ANN, both of which had a high score of 0.900 on Cardiovascular Disease Prognostic datasets. Based on the results of most clustering methods, such as k-means and hierarchical clustering, Cardiovascular Disease Prognostic datasets can be divided into two clusters. The prognostic accuracy of CVD depends on the accuracy of the proposed model in determining the diagnostic model. The more accurate the model, the better it can predict which patients are at risk for CVD.

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“…Given the vast amount of information available through the Internet of Things, patients are likely to receive a variety of recommendations for services or treatment of the disease. Therefore, choosing between these approaches requires a precise mechanism and effective recommending system [10][11][12][13].…”

Section: Introductionmentioning

confidence: 99%

Diagnosis of Chronic Diseases Based on Patients’ Health Records in IoT Healthcare Using the Recommender System

Nanehkaran

Licai

Chen

et al. 2022

Wireless Communications and Mobile Computing

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Due to the growth of IoT applications, especially health care, the information of patients’ health records using data collection from IoT-connected devices has been considered. Biological data of patients in the health record helps to monitor the patient’s status and identify various diseases. Chronic diseases are a type of silent disease that, if not diagnosed in time, can cause irreparable damage to patients. The use of patients’ medical record data for early diagnosis of chronic diseases has recently attracted the attention of many researchers. On the other hand, the application of machine learning methods in the form of recommender systems has taken an important step in improving medical services and health care. In this paper, a medical recommender system was presented to identify and treat chronic diseases using an IoT device. In the present method, the electronic patient health record dataset that is loaded in the PhysioNet data repository has been used. In the present dataset, patients’ health records have been recorded according to the identified diseases and the physician’s diagnosis. In the proposed method, the K -nearest neighbor classification method is used to identify the type of disease, and the collaborative filtering method is used to find the appropriate treatment for patients. The results of the implementation of the proposed method show that this approach, based on the use of symptom similarity among patients, has good accuracy in diagnosing and predicting chronic diseases and has provided higher results than previous methods.

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Anomaly Detection in Heart Disease Using a Density-Based Unsupervised Approach

Cited by 16 publications

References 18 publications

Early warning systems for malaria outbreaks in Thailand: an anomaly detection approach

Early warning systems for malaria outbreaks in Thailand: an anomaly detection approach

Hyperparameter optimization for cardiovascular disease data-driven prognostic system

Diagnosis of Chronic Diseases Based on Patients’ Health Records in IoT Healthcare Using the Recommender System

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