## 4 Discussion
### Contributions of Variables  
KDCTM is required to evaluate the effect of each parameter on the patient's results. This can be done by using RCA to comprehend individual needs. The impact ratios (IRs) of the features were determined for all patients in this study using RCA [31; 32; 39; 47]. In this way, we were able to define the contribution of the factors for each patient. Each candidate had a distinct distance variable effect. Table 13 outlines the contributing factors for the distances of the 10 patients being studied. In Table 13, the RCA output shows the contributions of all the candidate distance variables. The output was structured to prioritize the greater distances in descending order. It enabled us to determine the contributing factors individually. Traditional analytics will emphasize this, and drive the conduct of the population. KDCTM offers a personalized driver perspective with each patient's effects. If viewed individually, P9 was heavily influenced by X4 and X9, which were the Stance Time and Double Support Time, respectively. As for P10, X6 and X9 affected the element in an abnormal way. This proved the importance of patient care tailored to each patient's needs. The relationships established in this study can serve as a prescription for patients to consider corrective activities to decrease the influence of these factors on their general health. These essential features will be discussed in detail. In terms of the parameters, X3 (step width) was the most important feature, followed by X6 (single support) and X9 (double support). X3 had the highest IR for P6 (69%), P3 (66%), P1 (62%), P4 (53%), and P5 (36%). The step width refers to the distance between the rear end of the right and left heel center lines along the mediolateral axis. A base of support domain was characterized by the step width and step width variability [48; 49; 50; 51]. An increased step width indicates a loss of automaticity in walking that would render an individual more vulnerable to falling. Therefore, the step width is used as a parameter to evaluate the risk of falling and dementia. Under dual-task conditions, the step width can be also used to test the executive function in ACLR patients. X3 variability reflected that step width had the highest IR for P1, P5, P3, P4, P6, P2, and P8. It can also be used to identify peripheral sensory impairments that lead to walking malfunction [52]. Other studies characterized increased step as a feature of ACLR patients who are fearful of falling, but also noted that the measure width itself is not a predictor of falling [53; 54]. In comparison, decreased and increased step width variability were both linked to a history of falls. While the correlation between the step width variability of an ACLR knee and the drop risk is not clear, the results of this study revealed that the analysis of a base-of-support variable is justified [55; 56]. The ACLR knees demonstrated reduced single support (X6) phases in comparison to healthy knees. This reflects the prolonged time in which the ACLR knees remain in flexion prior to maximum extension to prevent a sudden weight change. This adaptation mechanism has been also shown by an electromyography experiment, in which ACLR patients were shown to have prolonged firing of biceps, femoris, and vastus medialis during the stance phase in comparison to the HG [57]. X9 reflected Double Support, and had the highest IR for P10, P9, P4, and P2. The stance phase can be divided into sub phases, namely, first double support (both feet in contact with the floor), single support (one foot in contact with the floor), and second double support [58]. Compared with an uninjured leg, individuals with ACLR spent less time in double-limb support during late adaptation. However, the ACLR group was capable of adjusting this parameter in such a way that the PG leg spent an increased amount of time in double support during the de-adaptation period [59; 60; 61; 62].