Disease prediction in data mining using association rule mining and keyword based clustering algorithms

Ramasamy, Subburaj; Nirmala, K.

doi:10.1080/1206212x.2017.1396415

Cited by 22 publications

(11 citation statements)

References 6 publications

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“…Noguchi et al [ 65 , 66 ] used ARM to find adverse events caused by drug-drug interactions. Ramasamy and Nirmala [ 67 ] applied ARM with an additional keyword-based clustering technique to predict disease. Kamalesh et al [ 68 ] predicted the risk of diabetes mellitus using ARM.…”

Section: Related Workmentioning

confidence: 99%

Discovering symptom patterns of COVID-19 patients using association rule mining

Tandan

Acharya

Pokharel

et al. 2021

Computers in Biology and Medicine

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Background The COVID-19 pandemic is a significant public health crisis that is hitting hard on people's health, well-being, and freedom of movement, and affecting the global economy. Scientists worldwide are competing to develop therapeutics and vaccines; currently, three drugs and two vaccine candidates have been given emergency authorization use. However, there are still questions of efficacy with regard to specific subgroups of patients and the vaccine's scalability to the general public. Under such circumstances, understanding COVID-19 symptoms is vital in initial triage; it is crucial to distinguish the severity of cases for effective management and treatment. This study aimed to discover symptom patterns and overall symptom rules, including rules disaggregated by age, sex, chronic condition, and mortality status, among COVID-19 patients. Methods This study was a retrospective analysis of COVID-19 patient data made available online by the Wolfram Data Repository through May 27, 2020. We applied a widely used rule-based machine learning technique called association rule mining to identify frequent symptoms and define patterns in the rules discovered. Result In total, 1,560 patients with COVID-19 were included in the study, with a median age of 52 years. The most frequently occurring symptom was fever (67%), followed by cough (37%), malaise/body soreness (11%), pneumonia (11%), and sore throat (8%). Myocardial infarction, heart failure, and renal disease were present in less than 1% of patients. The top ten significant symptom rules (out of 71 generated) showed cough, septic shock, and respiratory distress syndrome as frequent consequents. If a patient had a breathing problem and sputum production, then, there was higher confidence of that patient having a cough; if cardiac disease, renal disease, or pneumonia was present, then there was a higher confidence of septic shock or respiratory distress syndrome. Symptom rules differed between younger and older patients and between male and female patients. Patients who had chronic conditions or died of COVID-19 had more severe symptom rules than those patients who did not have chronic conditions or survived of COVID-19. Concerning chronic condition rules among 147 patients, if a patient had diabetes, prerenal azotemia, and coronary bypass surgery, there was a certainty of hypertension. Conclusion The most frequently reported symptoms in patients with COVID-19 were fever, cough, pneumonia, and sore throat; while 1% had severe symptoms, such as septic shock, respiratory distress syndrome, and respiratory failure. Symptom rules differed by age and sex. Patients with chronic disease and patients who died of COVID-19 had severe symptom rules more specifically, cardiovascular-related symptoms accompanied by pneumonia, fever, and cough as consequents.

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Section: Related Workmentioning

confidence: 99%

Discovering symptom patterns of COVID-19 patients using association rule mining

Tandan

Acharya

Pokharel

et al. 2021

Computers in Biology and Medicine

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“…Generally, medical data is composed of various relevant features and categorized based on its usefulness, i.e., less practical and not useful features (redundant features) based on its progression and formulation of diverse practice measurements. The predictions of these attributes are essential for representing the proximity of the domain appropriately [16]. The labelling process is a nontrivial task as the specification of the prediction column is not influenced by the irrelevant features.…”

Section: Related Workmentioning

confidence: 99%

Modelling an Efficient Clinical Decision Support System for Heart Disease Prediction Using Learning and Optimization Approaches

Kannan¹

2022

Computer Modeling in Engineering &Amp; Sciences

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With the worldwide analysis, heart disease is considered a significant threat and extensively increases the mortality rate. Thus, the investigators mitigate to predict the occurrence of heart disease in an earlier stage using the design of a better Clinical Decision Support System (CDSS). Generally, CDSS is used to predict the individuals' heart disease and periodically update the condition of the patients. This research proposes a novel heart disease prediction system with CDSS composed of a clustering model for noise removal to predict and eliminate outliers. Here, the Synthetic Over-sampling prediction model is integrated with the cluster concept to balance the training data and the Adaboost classifier model is used to predict heart disease. Then, the optimization is achieved using the Adam Optimizer (AO) model with the publicly available dataset known as the Stalog dataset. This flow is used to construct the model, and the evaluation is done with various prevailing approaches like Decision tree, Random Forest, Logistic Regression, Naive Bayes and so on. The statistical analysis is done with the Wilcoxon rank-sum method for extracting the p-value of the model. The observed results show that the proposed model outperforms the various existing approaches and attains efficient prediction accuracy. This model helps physicians make better decisions during complex conditions and diagnose the disease at an earlier stage. Thus, the earlier treatment process helps to eliminate the death rate. Here, simulation is done with MATLAB 2016b, and metrics like accuracy, precision-recall, F-measure, p-value, ROC are analyzed to show the significance of the model.

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“…Association rules (ARs) is an important data mining method in the medical field as it searches for interesting patterns and relationships between diseases and symptoms. The use of association rules has been extended to develop predictive models [40]. Association rules were used to find associations between perfusion measurement attributes of activated brain areas for early diagnosis of Alzheimer's disease in [41].…”

Section: Related Researchmentioning

confidence: 99%

An Efficient Association Rule Mining From Distributed Medical Databases for Predicting Heart Diseases

et al. 2021

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Electronic Health Records (EHRs) are aggregated, combined and analyzed for suitable treatment planning and safe therapeutic procedures of patients. Integrated EHRs facilitate the examination, diagnosis and treatment of diseases. However, the existing EHRs models are centralized. There are several obstacles that limit the proliferation of centralized EHRs, such as data size, privacy and data ownership consideration. In this paper, we propose a novel methodology and algorithm to handle the mining of distributed medical data sources at different sites (hospitals and clinics) using Association Rules. These medical data resources cannot be moved to other network sites. Therefore, the desired global computation must be decomposed into local computations to match the distribution of data across the network. The capability to decompose computations must be general enough to handle different distributions of data and different participating nodes in each instance of the global computation. In the proposed methodology, each distributed data source is represented by an agent. The global association rule computation is then performed by the agent either exchanging some minimal summaries with other agents or travelling to all the sites and performing local tasks that can be done at each local site. The objective is to perform global tasks with a minimum of communication or travel by participating agents across the network, this will preserve the privacy and the security of the local data. The proposed association rule mining methodology will be used for heart disease prediction using real heart disease data. These real data exist at different clinics and cannot be moved to a central site. The proposed model protects the patient data privacy and achieves the same results as if the data are moved and joined at a central site. We also validate the extracted association rules from all the data providers using an independent test datasets.INDEX TERMS Association rules, medical distributed databases, electronic health care records, agents, data privacy.

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Disease prediction in data mining using association rule mining and keyword based clustering algorithms

Cited by 22 publications

References 6 publications

Discovering symptom patterns of COVID-19 patients using association rule mining

Discovering symptom patterns of COVID-19 patients using association rule mining

Modelling an Efficient Clinical Decision Support System for Heart Disease Prediction Using Learning and Optimization Approaches

An Efficient Association Rule Mining From Distributed Medical Databases for Predicting Heart Diseases

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