Classification and clustering are crucial data categorization methods that are utilized to categorize objects into or more classes on the basis of their features. They seem to be similar processes as the difference is minute. In regards to classification, there are labels that are predefined and are assigned to each instance of input as per their properties while there are no labels in clustering (Classification vs. Clustering, 2019). Similarly, the other difference is that classification is used for supervised learning, while clustering is used for unsupervised learning. However, both techniques are essential in data mining as they significantly aid in making sense of the raw data.
One of the classification methods that is discussed is neural networks. This tries to mimic the intricate methodologies of the human brain and is thus among the most sought-after methods of data processing. Neural networks can be utilized extensively, particularly within the context of healthcare, in order to predict patient data. In organizations like UT Health Hospital in Tyler, Texas, neutral networks can prove to be of immense help, especially in the classification of data. With neural networks, the diagnostic process can be significantly decreased by narrowing down to specific diseases and by also providing crucial details on that particular disease. For instance, neural networks can be used to predict the likelihood of death of the patient from a certain disease when provided with particular conditions. This can aid the physicians in directing care where needed and also concentrating on those that need attention. Therefore, neural networks can be utilized extensively in predictive analysis and providing robust points of action where critical decisions can be made. In other cases, neural networks can also be used as the basis for building an artificially intelligent system that can also be used in the accurate detection of diseases, including the diagnoses of cancers and other diseases.
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Among the discussed methods of clustering is the utilization of K-means algorithms, which clusters data according to the similarities they share. The primary goal of this algorithm is to determine the cluster centers in such a way that observations in a particular cluster are similar to one another, while observations in other clusters are dissimilar (Albright and Winston, 2017). Within the context of healthcare, especially in the UT Health Hospital, this algorithm can be utilized in clustering patient data into distinct groups with those that share similarities, thus making it easy to compare different data points. For instance, different patient data can be clustered into those that are below eighteen years and those that are above eighteen years so that it can be simple to compare and contrast adults from young ones from the data points. Similarly, other instances of clustering include grouping different patient data into those that were diagnosed with Covid 19 and those that were not. There are endless possibilities with the clustering of data, but they can all come in handy, especially in the case of grouping data into groups with similarities and dissimilarities.
Data classification and clustering can prove to be of significant advantage to health institutions like UT Health Hospital as they can provide different data points that can be used in the making of decisions. Both methods can be utilized in pattern identification in machine learning, and thus organizations like UT Health Hospital can benefit from the process by being able to make sense of the huge chunk of patient data and thus find better means of augmenting patient care while also maintaining the integrity of their data. However, these are not the only methods of data mining as there are other extensive means and methodologies of mining data, particularly within the context of healthcare.
References
Albright, S. C., & Winston, W. L. (2017). Business analytics: Data analysis and decision making (6th ed.). Stamford, CT: Cengage Learning.
Classification vs. Clustering. (2019). Retrieved 13 August 2020, from https://www.geeksforgeeks.org/ml-classification-vs-clustering/
