# Clustering of a Health Dataset Using Diagnosis Co-Occurrences
## 3 Results
### Selection of the Clustering Solution
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Cluster Identification** & & **Cluster Identification** \\
**N (\%)** & **Block of Diagnoses (Prevalence** & **N (\%)** & **Block of Diagnoses (Prevalence** \\ MR (RR\_intra/ & **>2\%) : MR (RR\_intra/ RR\_inter)** & MR (RR\_intra/ & **>2\%) : MR (RR\_intra/ RR\_inter)** \\ RR\_inter) & & RR\_inter) \\ \hline  & R10-R19 (37.1\%): 2.24 (1.42/0.63) & \\  & K55-K64 (16.2\%): 1.09 (0.87/0.80) & \\  & N80-N98 (8.7\%): 3.13 (2.01/0.64) & \\
**1: Digestive disorders,** & N20-N23 (6.9\%): 1.06 (0.67/0.63) & \\ pregnancy, & O00-O08 (5.3\%): 5.06 (2.88/0.57) & \\
**menstruation** & N30-N39 (4.9\%): 1.22 (1.33/1.09) & **9: Feet Trauma** \\ N = 17705 (15.5\%) & Z30-Z39 (4.1\%): 2.85 (2.91/1.02) & \\ MR = **1.91 (1.48/0.77)** & O20-O29 (3.8\%): 3.59 (3.10/0.86) & \\  & K80-K87 (3.8\%): 1.34 (0.97/0.72) & \\  & \(N10\)-N16 (3.7\%): 0.94 (1.00/1.07) & \\  & N70-N77 (2.1\%): 1.71 (1.75/1.03) & \\ \hline
**2: General symptoms** & R50-R69 (68.4\%): 1.33 (0.97/0.73) & \\  & G40-G47 (11.1\%): 1.62 (1.01/0.62) & \\  & _R25-R29 (7.7\%): 0.92 (1.39/1.50)_ & \\
**N = 14336 (12.5\%)** & 160-I69 (6.0\%): 1.16 (0.92/0.80) & \\ MR = **1.25 (1.00/0.80)** & H80-H83 (5.2\%): 1.20 (0.68/0.57) & \\ \hline  & J00-J06 (21.7\%): 2.95 (1.74/0.59) & \\  & Z70-Z76 (13.8\%): 1.37 (1.14/0.83) & \\  & J40-J47 (8.6\%): 1.28 (1.36/1.07) & \\  & H65-H75 (7.8\%): 2.07 (1.70/0.82) & \\  & J20-J22 (7.4\%): 2.16 (1.84/0.85) & \\
**3: Infectious diseases** & A00-A09 (7.3\%): 2.36 (1.75/0.74) & \\  & K50-K52 (6.1\%): 1.82 (1.58/0.86) & \\ MR = **1.79 (1.48/0.82)** & T66-T78 (4.4\%): 1.21 (0.72/0.59) & \\  & K20-K31 (4.2\%): 1.01 (1.22/1.21) & \\  & L50-L54 (3.6\%): 1.80 (0.89/0.49) & \\  & B00-B09 (3.4\%): 1.96 (2.03/1.04) & \\  & R20-R23 (3.4\%): 1.18 (1.21/1.03) & \\  & L20-L30 (2.2\%): 1.60 (1.44/0.90) & \\ \hline  & R00-R09 (48.4\%): 1.55 (1.19/0.77) & \\
**4: General symptoms** & J09-J18 (12.8\%): 1.43 (1.28/0.90) & \\
**4: General symptoms** & J30-I52 (7.9\%): 1.84 (1.92/1.04) & \\  & E79-E90 (3.5\%): 1.33 (1.34/1.00) & \\  & D0-D604 (2.6\%): 1.02 (1.80/1.76) & \\ MR = **1.40 (1.36/0.97)** & D50-D53 (2.5\%): 1.41 (1.67/1.19) & \\  & E10-E16 (2.3\%): 1.18 (1.00/0.85) & \\  & I20-I25 (2.2\%): 2.54 (1.41/0.56) & \\ \hline  & R40-R46 (17.9\%): 1.44 (1.27/0.88) & \\  & Z00-Z13 (15.8\%): 1.50 (1.22/0.82) & \\  & S50-S59 (15.4\%): 0.58 (0.47/0.82) & \\
**5: Mental disorders and** & F10-F19 (11.0\%): 1.04 (1.71/1.64) & \\
**at-risk behaviours** & F40-F49 (10.3\%): 1.74 (1.69/0.97) & \\ N = **12139 (10.6\%)** & T36-T50 (8.6\%): 3.06 (2.30/0.75) & \\ MR = **1.65 (1.51/0.91)** & F30-F39 (7.9\%): 3.82 (2.78/0.73) & \\
**ST-155** (3.4\%): 1.04 (0.60/0.57) & \\  & F20-E92 (3.3\%): 4.54 (3.42/0.75) & \\  & S10-S19 (3.3\%): 1.37 (0.76/0.55) & \\  & F90-F98 (2.1\%): 3.24 (3.69/1.14) & \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Diagnostic content and quality indicators of the 16 clusters. As illustrated in Table 2 and Figure 2, the clustering solution obtained has a non-uniform spread in terms of visit population, with the frequency of visits gradually decreasing from 15.5% to 1.0%. Indeed, out of the 16 clusters found, we can distinguish a first group with the five biggest clusters, which have similar numbers of visits ranging from 17,705 (15.5%) to 12,139 (10.6%), accounting for 72,108 (63.0%) common visits of the total 114,391 visits; and a second group, with the 11 others, which have several visits steadily decreasing from 6777 (5.9%) to 1151 (1.0%), accounting for 42,509 (37.1%) common visits. Out of the 917 (0.8%) visits with multiple diagnoses, 624 (0.5%) were classified in two clusters and one in three clusters, hence the association between visits and clusters is close to bijective.