One of the main uses of k-clustering in the healthcare sector is the study of patient disease patterns using the approach. It can be used in developing and determining the most suitable diagnosis and treatment interventions for patients with different characteristics. For instance, in choosing the kind of cancer treatment to administer to patients in different stages, k-means clustering can be used to determine the interventions based on disease duration and symptoms. Cancer is one of the leading causes of death in the United States (US). K-means clustering can be used to fight cancer more effectively. Healthcare providers enhance their ability to detect and diagnose diseases in the early stages. Ensuring early diagnosis of various diseases such as cancer reduces the mortality rates associated with various types of cancer. For instance, cervical cancer can be detected early and treated to prevent advancement into later stages that have higher mortality rates.
Apart from early diagnosis, k-means clustering also allows healthcare providers to assign more effective therapies based on the patient’s genetic makeup. The drugs can be tailored to specific patient’s needs, thus providing regulated drug doses that minimize or eliminate adverse drug reactions and side effects. K-means clustering can be used to support parallelization and assist in mapping DNA pairs. K-means clustering can also be used in selecting diabetes interventions based on the duration and extent of the disease, where clusters will be defined based on the extent of peripheral neuropathy. Using k-means clustering, healthcare providers can develop a deeper understanding of patient disease patterns. This will help healthcare providers to improve the efficiency of patient care. K-means clustering is also important in healthcare since it provides healthcare providers with decision support (Khanmohammadi et al., 2017). They have access to vast data sets that can be used to determine the way forward in terms of healthcare administration.
Delegate your assignment to our experts and they will do the rest.
The approach can also be used to help smoothen the administration process. K-means clustering can also be used to determine the risk factors associated with water supplies based on the mineral content. For instance, it can be used to identify and analyze the possible risk factors related to the fluoride content in the water. Suitable patterns can be developed to assist in the decision-making process. Additionally, k-means clustering can also be used to classify weight to identify various infections such as human immunodeficiency virus (HIV), leukaemia, inflammatory, and bacterial or viral infections. Diseases can be predicted using the approach (Ogbuabor & Ugwoke, 2018).
Problem 21
The cities in cluster1 are high-income cities, the least jobs, and are among cities with the lowest transportation networks. They also have the second-lowest education levels and climate. Crime rates in cities in cluster 1 are the second-lowest among the cities. They are the second in the provision of healthcare services. Cities in cluster 2 have the second-lowest-cost of living. They have the second-highest transportation network. They have the highest jobs and second-highest education. Cities in cluster 3 have the highest cost of living, the lowest transportation networks, and the best climate. The clustering makes sense since it provides ranks in terms of the defined parameters. It can be used to classify the cities based on the available resources.
References
Khanmohammadi, S., Adibeig, N., & Shanehbandy, S. (2017). An improved overlapping k-means clustering method for medical applications. Expert Systems with Applications , 67 , 12-18.
Ogbuabor, G., & Ugwoke, F. N. (2018). Clustering algorithm for a healthcare dataset using silhouette score value. International Journal of Computer Science & Information Technology , 10 (2), 27-37.
