# Towards a Machine Learning Model for Detection of Dementia Using Lifestyle Parameters
## 2 Materials and Method
### Work Plan
#### 2.3.3 Shortlisting Parameters
We also carried out temporal analysis of the above shortlisted parameters to see how they vary with time. While the NHATS dataset includes subjects who participate in multiple rounds, it does not inherently support temporal analysis. There is no formal mechanism for tracking an individual's progression across multiple rounds unless we undertake a multi-step data aggregation process. Our initial step involved aggregating subjects based on their subject-IDs across these multiple rounds, resulting in the creation of an individual-specific data frame (as illustrated in Figure 2). Furthermore, the trends in the data did not become readily discernible until we employed techniques such as lasso regression. We ensured that the data were a subset of total data to ensure that we only had data from individuals that had participated in all 10 rounds. The number decreases with each round because individual participants may have expired between these 10 rounds. We created individual data frames for everyone, consisting of the parameters common in all 10 rounds, which are given in Figure 2. \begin{table}
\begin{tabular}{l l l l} \hline
**Parameter** & **Description** & **Possible Values** & **Correlation** \\ \hline flag\_dem & Label indicating dementia & 1—dementia & 1 \\  & & 2—Not dementia & 1 \\  & & 1—ALWAYS DID IT BY SELF & \\  & & 2—ALWAYS DID IT TOGETHER WITH & \\ mc9 medstrk & Needs assistance for medication & SOMEONE ELSE & 0.3634 \\  & & 3—SOMEONE ELSE ALWAYS DID IT & \\  & & 4—IT VARIIED (MORE THAN ONE WAY) & \\  & & 5—NOT DONE IN LAST MONTH & \\ \hline cg9 presidna2 & Ability to recall the last name of the president & 1—Last name correct & \(-\)0.3459 \\  & & 2—Last name incorrect & \\ \hline cg9 dwrddlyrc & Delayed word recall & Number of correct words & \(-\)0.3399 \\ \hline cg9 dwrdimmrc & Immediate word recall & Number of correct words & \(-\)0.3373 \\ \hline Cg9 probecca3 & Problem recalling date & 0—No problem & 0.3312 \\  & & 1—Problem during recall & \\ \hline cg9 presidna4 & Ability to recall the first name of the & 3—First Name correct & \(-\)0.3249 \\  & president & 4—First Name incorrect & \\ \hline cg9 dclkdraw & Ability to draw a clock & Ranged Integer from 1–5 & \(-\)0.3214 \\ \hline sc9 bathlip & Needs help while bathing & 1—yes & \(-\)0.3153 \\  & & 2—No & \\ \hline wb9 agrwstmtl & Perception of control of one’s own life & 1—Agree A Lot, 2—Agree a little, 3—Agree & \(-\)0.3141 \\  & & not at all & \\ \hline sc9 dreshlp & Needs help while dressing & 1—Yes & \(-\)0.3031 \\  & & 2—No & \\ \hline mo9 insdhlp & Needs help moving inside the house & 1—Yes & \(-\)0.3029 \\  & & 2—No & \\ \hline mo9 douthelp & Needs help moving outside house & 1—Yes & 0.3016 \\  & & 2—No & \\ \hline Cg9 probecca5 & Problem recalling month & 1—Yes & 0.3001 \\ \hline \end{tabular}
\end{table} 
Table 7: Statistical Parameters. For each individual data frame, we applied [PERSON]'s correlation to explain how these parameters vary in relation to dementia progression. We thus obtained individual correlation frame for each of the 2251 subjects. We use lasso regression methodology to identify relation and temporal variability of these parameters. Lasso regression serves as a regularization method, preferred over standard regression for enhanced prediction accuracy [30]. Operating via shrinkage, it pulls data points closer to the mean. Lasso promotes simpler, sparser models with fewer parameters, making it valuable for automated model selection tasks such as variable elimination. We used these individual data frames as input to the lasso regression methodology to find how these parameters vary with time. We found that the parameters listed in Table 9 had maximum impact. Temporal analysis affirms that the parameters shortlisted from Round 9 data, using domain methods and statistical methods, are valid for all the rounds.