An algorithm of apriori based on medical big data and cloud computing

Cui, Xiaoyan; Yang, Shimeng; Wang, Darning

doi:10.1109/ccis.2016.7790284

Cited by 10 publications

(3 citation statements)

References 5 publications

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“…• Find the relations between time-independent and time-dependent parameters using Apriori algorithm [28], • Create the list of most appropriative medicaments from medicaments with support level higher than min_support. The results of algorithm for outliers mining and personalizing the treatment have been evaluated by the experts with more than 20 years' experience in purulent surgical infection of soft tissues, antibiotic therapy in the treatment of diseases caused by purulent surgical infection.…”

Section: Resultsmentioning

confidence: 99%

Data Driven Analytics for Personalized Medical Decision Making

Melnykova¹,

Shakhovska²,

ml.³

et al. 2020

Preprint

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The study was conducted on applying machine learning and data mining methods to personalizing the treatment. This allows investigating individual patient characteristics. Personalization is built on the clustering method and associative rules. It was suggested to determine the average distance between instances for optimal performance metrics finding. The formalization of the medical data pre-processing stage for finding personalized solutions based on current standards and pharmaceutical protocols is proposed. The model of patient data is built. The paper presents the novel approach to clustering built on ensemble of cluster algorithm with better than k-means algorithm Hopkins metrics. The personalized treatment usually is based on decision tree. Such approach requires a lot of computation time and cannot be paralyzed. Therefore, it is proposed to classify persons by conditions, to determine deviations of parameters from the normative parameters of the group, as well as the average parameters. This made it possible to create a personalized approach to treatment for each patient based on long-term monitoring. According to the results of the analysis, it becomes possible to predict the optimal conditions for a particular patient and to find the medicaments treatment according to personal characteristics.

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Section: Resultsmentioning

confidence: 99%

Data Driven Analytics for Personalized Medical Decision Making

Melnykova¹,

Shakhovska²,

ml.³

et al. 2020

Preprint

View full text Add to dashboard Cite

show abstract

“…According to the results of the research, it is possible to point out the significance of such factors as the heterogeneity of data, subjective assessments of psychological indicators, and temporal changes in the characteristics of the general state of the investigated object. The medical data processing also requires the development of appropriate solutions for data collection, transmission, storage, and protection [19,21,32,35]. On one hand, most of analyzed algorithms solve the above problems effectively, but on other hand do not cover the entire list of problems associated with the personalized treatment.…”

Section: Discussionmentioning

confidence: 99%

Data-Driven Analytics for Personalized Medical Decision Making

et al. 2020

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The study was conducted by applying machine learning and data mining methods to treatment personalization. This allows individual patient characteristics to be investigated. The personalization method was built on the clustering method and associative rules. It was suggested to determine the average distance between instances in order to find the optimal performance metrics. The formalization of the medical data preprocessing stage was proposed in order to find personalized solutions based on current standards and pharmaceutical protocols. The patient data model was built using time-dependent and time-independent parameters. Personalized treatment is usually based on the decision tree method. This approach requires significant computation time and cannot be parallelized. Therefore, it was proposed to group people by conditions and to determine deviations of parameters from the normative parameters of the group, as well as the average parameters. The novelty of the paper is the new clustering method, which was built from an ensemble of cluster algorithms, and the usage of the new distance measure with Hopkins metrics, which were 0.13 less than for the k-means method. The Dunn index was 0.03 higher than for the BIRCH (balanced iterative reducing and clustering using hierarchies) algorithm. The next stage was the mining of associative rules provided separately for each cluster. This allows a personalized approach to treatment to be created for each patient based on long-term monitoring. The correctness level of the proposed medical decisions is 86%, which was approved by experts.

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“…Therefore, noise needs to be cleaned. Data mining methods are used for this method too [5].…”

Section: Introductionmentioning

confidence: 99%

Using Apriori Data Mining Method in COVID-19 Diagnosis

Çelik

2020

Journal of Engineering Technology and Applied Sciences

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Corona virus 2019 disease has spread all over the world and many people have died due to this disease. PCR (Polymerase Chain Reaction) tests are mostly applied to detect people who have this disease. However, in some cases, it is necessary to wait twenty-four hours for the results of this test. In such cases, the treatment and isolation process of the patient may be delayed. Therefore, the rapid commencement of treatment and isolation process by analyzing the symptoms, are of great importance. Using data mining methods can be carried out quickly specify analysis. Association rule algorithms are also among data mining methods. The most common SETM, AIS and apriori association rule algorithms are encountered. The most widely used is the apriori association algorithm. Using this algorithm, the frequency and association rates of the data are found in the data set. In this study, it has been shown that association rules calculated by apriori algorithm can be used in the diagnosis of COVID-19. By using the COVID-19 Survilance data set, the association rates of the disease symptoms specified in the ICD (International Classification of Diseases) International Classification of Diseases codes were determined. According to the results obtained; it has been observed that the patients with these symptoms are 100% definitely infected with COVID-19 disease when the disease symptoms represented by the A01, A02 and A04 disease codes are together.

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An algorithm of apriori based on medical big data and cloud computing

Cited by 10 publications

References 5 publications

Data Driven Analytics for Personalized Medical Decision Making

Data Driven Analytics for Personalized Medical Decision Making

Data-Driven Analytics for Personalized Medical Decision Making

Using Apriori Data Mining Method in COVID-19 Diagnosis

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