# Using Explainable AI (XAI) for the Prediction of Falls in the Older Population
## 2 Materials and Methods
### Input Features  
A total of 46 features were used. The majority of the input features were taken from the previously investigated Syncope-Falls Index (SYFI), a 40-deficit frailty index which was derived from the simple arithmetic sum of 40 health deficits selected from TILDA Wave 1 which was based on their clinically postulated likelihood of increasing the risk of syncope and falls within the older population [6]. Age and sex, which were not originally included in the SYFI, were also included as input features in the present work. The remaining 4 features, namely \"Fall in the last year\" (yes or no), \"Any previous history of blackout/faint\" (present or absent), \"Frequent fainter when young\" (present or absent), and \"Afraid of falling\" (present or absent) were included as additional features that were potentially associated with the future risk of falls.  
Except for age at the time of Wave 1 data collection, which was used as a continuous variable, all of the input features were binary, where the presence of the feature was recorded as \"1\" and its absence as \"0\". To ensure consistency with the previous SYFI study, only participants aged 65 years and over at the time of the first interview (Wave 1) were included. Table 1 summarises the 46 input features that were utilised in the present study. Details of the 40 SYFI deficits have been described elsewhere [6].  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Features** & **Type of Variable (Continuous, Binary)** & **Feature Value** \\ \hline
1. & Polypharmacy & Polypharmacy & 0 = Absent, 1 = Present \\
2. & Antihypertensives & Binary & 0 = Absent, 1 = Present \\
3. & Anticholinergics & Binary & 0 = Absent, 1 = Present \\
4. & Benzodiazepepines & Binary & 0 = Absent, 1 = Present \\
5. & \(Z\)-drugs & Binary & 0 = Absent, 1 = Present \\
6. & Antidepressants & Binary & 0 = Absent, 1 = Present \\
7. & Weight loss & Binary & 0 = Absent, 1 = Present \\
8. & Poor eyesight & Binary & 0 = Absent, 1 = Present \\
9. & Poor hearing & Binary & 0 = Absent, 1 = Present \\
10. & Poor smell & Binary & 0 = Absent, 1 = Present \\
11. & Poor taste & Binary & 0 = Absent, 1 = Present \\
12. & Hypertension & Binary & 0 = Absent, 1 = Present \\
13. & Angina & Binary & 0 = Absent, 1 = Present \\
14. & Myocardial infarction & Binary & 0 = Absent, 1 = Present \\
15. & Congestive cardiac failure & Binary & 0 = Absent, 1 = Present \\
16. & Heart murmur & Binary & 0 = Absent, 1 = Present \\
17. & Abnormal heart rhythm & Binary & 1 = Present \\
18. & Diabetes mellitus & Binary & 0 = Absent, 1 = Present \\
19. & Stroke & Binary & 1 = Present \\
20. & Transient ischemic attack & Binary & 0 = Absent, 1 = Present \\
21. & Grip weakness & Binary & 0 = Absent, 1 = Present \\
22. & Leg ulcers & Binary & 0 = Absent, 1 = Present \\
23. & Protein in urine & Binary & 0 = Absent, 1 = Present \\
24. & Leg numbness & Binary & 0 = Absent, 1 = Present \\
25. & Diabetic retinopathy & Binary & 1 = Present \\
26. & Diabetic nephropathy & Binary & 0 = Absent, 1 = Present \\
27. & Chronic obstructive pulmonary & Binary & 0 = Absent, 1 = Present \\ disease (COPD) & Asthma & Binary & 0 = Absent, 1 = Present \\
29. & Osteoarthritis & Binary & 0 = Absent, 1 = Present \\
30. & Osteoporosis & Binary & 0 = Absent, 1 = Present \\
31. & Cancer & Binary & 0 = Absent, 1 = Present \\
32. & Psychiatric problems & Binary & 0 = Absent, 1 = Present \\
33. & Alcohol or substance abuse & Binary & 0 = Absent, 1 = Present \\
34. & Cognitive impairment & Binary & 0 = Absent, 1 = Present \\
35. & Gastric ulcers & Binary & 0 = Absent, 1 = Present \\ \hline \hline \end{tabular}
\end{table}
Table 1: Forty-six input features.