DATA MINING CLUSTERING IN HEALTHCARE

The accumulating amounts of data are making traditional analysis methods impractical. Novel tools employed in Data Mining (DM) provide a useful alternative framework that addresses this problem. This research suggests a technique to identify certain patient populations. Our model examines the patient population and clusters certain groups. Those subpopulations are then classified in terms of their appropriate medical treatment. As a result, we show the value of applying a DM model to more easily identify patients

Data mining Techniques for Health Care: AReview

Data mining is gaining popularity in disparate research fields due to its boundless applications and approaches to mine the data in an appropriate manner. Owing to the changes, the current world acquiring, it is one of the optimal approach for approximating the nearby future consequences. Along with advanced researches in healthcare monstrous of data are available, but the main difficulty is how to cultivate the existing information into a useful practices. To unfold this hurdle the concept of data mining is the best suited. Data mining have a great potential to enable healthcare systems to use data more efficiently and effectively. Hence, it improves care and reduces costs. This paper reviews various Data Mining techniques such as classification, clustering, association, regression in health domain. It also highlights applications, challenges and future work of Data Mining in healthcare

Towards Unsupervised Detection of Process Models in Healthcare

Process mining techniques can play a significant role in understanding healthcare processes by supporting analysis of patient records in electronic health record systems. Healthcare processes are complex and patterns of care may vary considerably within similar cohorts of patients. Process mining often creates "spaghetti" models and require significant domain expert input to refine. Machine learning approaches such as Hidden Markov Models (HMM) may assist this refinement process. HMMs have been advocated for patient pathways clustering purposes; however these models can also be utilized for detecting hidden processes to help event abstraction. We explore use of an unsupervised method for detecting hidden healthcare sub-processes using HMMs, in particular the Viterbi algorithm. We describe an approach to enrich the event log with HMM-derived states and remodeling the healthcare processes as state transitions using a process mining tool. Our method is applied to event data for 'Altered Mental Status' patients that was extracted from a US hospital database (MIMIC-III). The results are promising and show a successful reduction of model complexity and detection of several hidden processes unsupervised by a domain expert

A Review on K-means Clustering Based on Quantum Particle Swarm Optimisation Algorithm

Unsupervised learning clustering techniques play a vital role in data mining, with a wide range of applications in unsupervised classification. Clustering is a method used to categorise data into meaningful groups. The k-means algorithm is a well-known clustering algorithm that aims to minimise the squared distance between feature values of points within the same cluster. In many applications, using an evolutionary computation technique called Quantum Particle Swarm Optimization (QPSO) in conjunction with the k-means algorithm has proven effective in finding suboptimal solutions. In this algorithm, the cluster centres are simulated as particles, allowing for the identification of suitable and stable cluster centres. This paper discusses the current improvement in the QPSO-k-means clustering algorithm, focusing on swarm initialisation and algorithm parameter optimisation. We validate the algorithm using the UCI healthcare dataset and demonstrate its ability to address suboptimal clustering by optimising parameters such as the number of iterations, error rate, and optimal solution for cluster centres. The minimisation factor of the validation parameter indicates the compactness and validity of the clustering algorithm