Table 1.
Overall performance measure of classification algorithms on datasets
| Algorithms | T1D | Az | Ab | Asthma | A & B | A & C | B & D | Avg. | Rank |
|---|---|---|---|---|---|---|---|---|---|
|
Naïve Bayes |
92.0 |
93.4 |
91.5 |
77.7 |
90.8 |
93.5 |
93.6 |
90.4 |
1 |
|
MLP |
90.1 |
92.7 |
90.2 |
71.1 |
84.7 |
92.7 |
89.3 |
87.3 |
2 |
|
SVM |
91.6 |
88.0 |
90.7 |
71.3 |
86.1 |
88.4 |
93.1 |
87.0 |
3 |
|
VFI |
90.5 |
92.2 |
75.5 |
62.6 |
87.7 |
93.4 |
92.7 |
84.9 |
4 |
|
Hyper Pipes |
89.8 |
89.7 |
81.3 |
62.3 |
82.0 |
86.6 |
87.8 |
82.8 |
5 |
|
R. Forest |
91.5 |
82.4 |
93.3 |
62.8 |
80.6 |
81.4 |
81.1 |
81.9 |
6 |
|
Bayes Net |
90.3 |
87.7 |
92.5 |
53.9 |
80.2 |
83.2 |
85.1 |
81.8 |
7 |
|
K-means |
88.3 |
91.8 |
80.7 |
59.6 |
77.8 |
83.3 |
83.6 |
80.7 |
8 |
|
Logistic R. |
90.6 |
93.3 |
60.4 |
50.7 |
81.5 |
84.8 |
90.7 |
78.9 |
9 |
|
SLR |
92.2 |
71.8 |
90.1 |
72.2 |
65.0 |
68.5 |
84.7 |
77.8 |
10 |
|
KNN |
91.4 |
81.5 |
52.5 |
55.8 |
87.5 |
75.7 |
89.0 |
76.2 |
11 |
|
K star |
81.9 |
90.7 |
89.4 |
53.5 |
64.3 |
68.8 |
70.7 |
74.2 |
12 |
|
M5P |
85.1 |
58.7 |
83.2 |
60.0 |
75.2 |
73.4 |
79.6 |
73.6 |
13 |
|
J48 |
80.3 |
69.7 |
78.4 |
48.7 |
70.6 |
68.4 |
76.7 |
70.4 |
14 |
|
Random Tree |
83.8 |
71.7 |
76.2 |
52.9 |
69.3 |
60.8 |
75.0 |
70.0 |
15 |
|
ASC |
76.8 |
70.0 |
77.9 |
43.1 |
72.0 |
63.1 |
76.7 |
68.5 |
16 |
| LDA | 69.7 | 52.0 | 89.1 | 70.8 | 62.8 | 69.7 | 52.6 | 66.7 | 17 |
T1D: Type 1 diabetes datasets, Az: Alzehemer’s dataset, Ab: Antibodies dataset. Table showing algorithms overall performance in each datasets based on average score. Score >90% are marked in bold. Naïve Bayes scored the overall highest average score of 90.4%.