Selection of data analytic techniques by using fuzzy AHP TOPSIS from a healthcare perspective

The healthcare industry has been put to test the need to manage enormous amounts of data provided by various sources, which are renowned for providing enormous quantities of heterogeneous information. The data are collected and analyzed with different Data Analytic (DA) and machine learning algorithm approaches. Researchers, scientists, and industrialists must manage or select the best approach associated with DA in healthcare. This scientific study is based on decision analysis between the DA factors and alternatives. The information affects the whole system in a rational manner. This information is very important in healthcare sector for appropriate prediction and analysis. The evaluation discusses its benefits and presents an analytic framework. The Fuzzy Analytic Hierarchy Process (Fuzzy AHP) approach is used to address the weight of the factors. The Fuzzy Techniques for Order Preference by Similarity to Ideal Solution (Fuzzy TOPSIS) address the rank of the data analytic alternatives used in healthcare sector. The models used in the article briefly discuss the challenges of DA and approaches to address those challenges. The assorted factors of DA are capture, cleaning, storage, security, stewardship, reporting, visualization, updating, sharing, and querying. The DA alternatives include descriptive, diagnostic, predictive, prescriptive, discovery, regression, cohort and inferential analyses. The most influential factors of the DA and the most suitable approach for the DA are evaluated. The 'cleaning' factor has the highest weight, and 'updating' is achieved at least by the Fuzzy-AHP approach. The regression approach of data analysis had the highest rank, and the diagnostic analysis had the lowest rank. Decision analyses are necessary for data scientists and medical providers to predict diseases appropriately in the healthcare domain. This analysis also revealed the cost benefits to hospitals.