## 3 Proposed Hybrid Models  
The authors in this paper have presented hybrid MCDM models for ranking the various mHealth applications and for selecting the best applications. To begin with, a hierarchical structure was built utilizing 10 criteria and 29 sub-criteria that were determined after a review of literature and considering the opinions provided by experts. The FAHP's technique was utilized in this work, since it only requires the construction of a few pair-wise comparison matrices, rendering the method easier and more systematic in its application [37]. The current study utilized hybrid methodologies that combined the FAHP and two MCDM approaches, CODAS and MOORA, for the evaluation of the best T2 DM mHealth application. The methodologies were implemented as follows. The FAHP assessed the relative criteria and sub-criteria weights. These weights were then utilized in the MADM (CODAS) and MODM (MOORA) approaches to rate and rank the multiple mHealth alternativeswithin a fuzzy environment, by applying the fuzzy set theory for controlling the unpredictability of the evaluation processes. As a result, we concluded that using the FAHP, CODAS, and MOORA, the problem of picking the best T2 DM mHealth application could be solved. The two segments of the suggested integrated methodologies for finding the best T2 DM mHealth applications are shown in Figure 1.  
Figure 1: Proposed Hybrid Models. Diverse criteria and their associated sub-criteria were identified during the first segment of the FAHP, using various sources including a literature review and consideration of expert opinion. Expert opinion and pairwise comparison matrices were used to create a decision hierarchy structure using assessed criteria, associated sub-criteria, and choices. The weights related to the criteria and their associated sub-criteria were tabulated by employing the geometric mean (GM) methodology. In the second section, the ranking of the alternatives was accomplished by using the two methodologies, both of which used global weights. Global weights were the product of the criteria weight and associated sub-criteria weight. The two methodologies are:  
* CODAS: The negative ideal solutions were generated in this MADM technique. Using the fuzzy negative solution values, the Euclidean distance (ED) and the taxicab distance (TD) were determined, and the assessment scores (\(AS_{i}\)) for each alternative were tabulated. The choices were ranked in descending order. The ranking outcomes' consistency and validity were assessed using sensitivity analysis. * MOORA: The MODM technique was utilized to create the weighted normalized decision matrix and the normalized assessment values. The alternatives were ranked using the normalized assessment values. The following pseudo code Algorithm 1 describes the proposed hybrid models for evaluating the usability of T2 DM mHealth applications and ranking the best application. The proposed algorithm uses Fuzzy AHP for determination of criteria weights as mentioned in Algorithm 2. Algorithm 3 mentions the CODAS method, used for ranking the alternatives. MOORA method mentioned in Algorithm 4 below is also used for ranking of the alternatives. A comparison study has been conducted between these two hybrid MCDM models for checking the consistency. ```
1{
2// The following pseudocode is to select the best T2 DM mHealth application
3//among'n' number of applications based on usability criteria. 4 Input: Alternatives: 'n' number of different mHealth applications. (n\(\in\)R and
5n>1)
6 Criteria:'m' number of homogeneous features related to T2 DM mHealth
7 applications based on which the alternatives will be evaluated.