The Role of Big Data Mining in Healthcare Applications

Abstract

As technology is growing every day, the need for the technology is also becoming essential in every field. The amount of data generated by the healthcare industry is becoming tough to manage and to examine it in efficient manner for future use. In the healthcare field, massive amount of data is generated, from individual patient's information to health history, clinical data and genetic data. The analysis of patient's data is becoming more important, to evaluate the medical condition of patient and to prevent and take precautions for future. With the help of technology and computerized automation of machines, data can be analyzed in more efficient manner. Managing the huge volume of data has many problems interrelated to data security, data integrity and inconsistency. Process mining and data mining techniques have opened a new access for diagnosis of disease. Similarly, to provide effective treatment for a disease's triennial prevention, data mining can be used. Big data mining can aid in analyzing medical operation indicators of hospitals for a period to help hospital administrators provide data support for medical decision-making. In this manuscript, the various applications of big data mining techniques have been analyzed to improve the healthcare systems.

Clinical big data in healthcare: A survey of medical computers

“…The goal of analyzing this type of big data is to obtain useful applications that serve the community and raise the level of health care services. In previous research that aimed on the most critical applications in this field, it was mentioned on the results of researchers in the study [27], that the analysis of these big data has produced efficient applications such as: identification of health risks, disease surveillance, telemedicine, predictive analyzes and Precise strategic layouts. Other researchers also clarified in another study about these applications that their interest is in providing services related to all patients' needs and addressing community health issues with high efficiency [28].…”

Cluster Analysis of Hospital Inpatient Service Efficiency Based on BOR, BTO, TOI, AvLOS Indicators using Agglomerative Hierarchical Clustering

“…Penelitian di bidang kesehatan dan rumah sakit telah diprediksi akan meningkat seiring dengan bertumbuhnya data dari waktu ke waktu. Data medis bersumber dari data profil pasien, riwayat kontrol, tindak lanjut kontrol kesehatan dan data klinis [7]. Tseng, dkk meneliti tentang analisis klaster sumber data medis di rumah sakit distrik di Taiwan menggunakan data tahun 2007-2011.…”

“…Formula average linkage ditunjukkan pada Pers. (7), dimana 𝑑(𝐶 𝑖𝑗 ) adalah sebuah jarak dari 2 klaster kei dan j, lalu dihitung jaraknya terhadap 𝐶 𝑘 yaitu klaster berikutnya yang akan digabungkan berdasarkan jarak rata-rata. Terbentuklah calon klaster baru yang memiliki jarak rata-rata dari penjumlahan 𝑑(𝐶 𝑖 , 𝐶 𝑘 ) dan 𝑑(𝐶 𝑗 , 𝐶 𝑘 ) lalu dibagi 2.…”