Search is not available for this dataset
age float64 0.92 455 | sex float64 0 1.17 | on_thyroxine float64 0 1.13 | query_on_thyroxine float64 0 1.12 | on_antithyroid_medication float64 0 1.11 | sick float64 0 1.13 | pregnant float64 0 1.13 | thyroid_surgery float64 0 1.1 | I131_treatment float64 0 1.15 | query_hypothyroid float64 0 1.13 | query_hyperthyroid float64 0 1.16 | lithium float64 0 1.12 | goitre float64 0 1.14 | tumor float64 0 1.15 | hypopituitary float64 0 1.06 | psych float64 0 1.14 | TSH_measured float64 0 1.18 | TSH float64 0 530 | T3_measured float64 0 1.21 | T3 float64 0.05 11.5 | TT4_measured float64 0 1.2 | TT4 float64 1.9 442 | T4U_measured float64 0 1.18 | T4U float64 0.25 2.35 | FTI_measured float64 0 1.18 | FTI float64 1.91 421 | TBG_measured float64 0 0 | referral_source float64 0 4.77 | binaryClass float64 0 3 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
41 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1.3 | 1 | 2.5 | 1 | 125 | 1 | 1.14 | 1 | 109 | 0 | 1 | 1 |
23 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 4.1 | 1 | 2 | 1 | 102 | 0 | 0.98 | 0 | 107 | 0 | 4 | 1 |
46 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0.98 | 0 | 2 | 1 | 109 | 1 | 0.91 | 1 | 120 | 0 | 4 | 1 |
70 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0.16 | 1 | 1.9 | 1 | 175 | 0 | 0.98 | 0 | 107 | 0 | 4 | 1 |
70 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0.72 | 1 | 1.2 | 1 | 61 | 1 | 0.87 | 1 | 70 | 0 | 3 | 1 |
18 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0.03 | 0 | 2 | 1 | 183 | 1 | 1.3 | 1 | 141 | 0 | 4 | 1 |
59 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1.4 | 0 | 2 | 1 | 72 | 1 | 0.92 | 1 | 78 | 0 | 4 | 1 |
80 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 2.2 | 1 | 0.6 | 1 | 80 | 1 | 0.7 | 1 | 115 | 0 | 3 | 1 |
66 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0.6 | 1 | 2.2 | 1 | 123 | 1 | 0.93 | 1 | 132 | 0 | 3 | 1 |
68 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 2.4 | 1 | 1.6 | 1 | 83 | 1 | 0.89 | 1 | 93 | 0 | 3 | 1 |
84 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 1.1 | 1 | 2.2 | 1 | 115 | 1 | 0.95 | 1 | 121 | 0 | 3 | 1 |
67 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0.03 | 0 | 2 | 1 | 152 | 1 | 0.99 | 1 | 153 | 0 | 4 | 1 |
71 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0.03 | 1 | 3.8 | 1 | 171 | 1 | 1.13 | 1 | 151 | 0 | 4 | 1 |
59 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 2.8 | 1 | 1.7 | 1 | 97 | 1 | 0.91 | 1 | 107 | 0 | 3 | 1 |
28 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 3.3 | 1 | 1.8 | 1 | 109 | 1 | 0.91 | 1 | 119 | 0 | 1 | 1 |
65 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 12 | 0 | 2 | 1 | 99 | 1 | 1.14 | 1 | 87 | 0 | 4 | 0 |
42 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1.2 | 1 | 1.8 | 1 | 70 | 1 | 0.86 | 1 | 81 | 0 | 4 | 1 |
63 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1.5 | 1 | 1.2 | 1 | 117 | 1 | 0.96 | 1 | 121 | 0 | 3 | 1 |
80 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 6 | 1 | 1.6 | 1 | 99 | 1 | 0.95 | 1 | 104 | 0 | 3 | 1 |
28 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 2.1 | 1 | 2.6 | 1 | 121 | 1 | 0.94 | 1 | 130 | 0 | 1 | 1 |
51 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0.1 | 0 | 2 | 1 | 130 | 1 | 0.86 | 1 | 151 | 0 | 4 | 1 |
46 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0.8 | 1 | 2.1 | 1 | 108 | 1 | 0.91 | 1 | 119 | 0 | 4 | 1 |
81 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1.9 | 1 | 0.3 | 1 | 102 | 1 | 0.96 | 1 | 106 | 0 | 3 | 1 |
54 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 3.1 | 0 | 2 | 1 | 104 | 1 | 0.9 | 1 | 116 | 0 | 4 | 1 |
55 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0.2 | 1 | 1.8 | 1 | 134 | 1 | 1.02 | 1 | 131 | 0 | 3 | 1 |
63 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0.03 | 1 | 5.5 | 1 | 199 | 1 | 1.05 | 1 | 190 | 0 | 4 | 1 |
60 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 13 | 1 | 1.4 | 1 | 57 | 1 | 0.62 | 1 | 92 | 0 | 4 | 1 |
25 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0.3 | 1 | 3.1 | 1 | 129 | 0 | 0.98 | 0 | 107 | 0 | 4 | 1 |
73 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1.9 | 1 | 1.5 | 1 | 113 | 1 | 1.06 | 1 | 106 | 0 | 3 | 1 |
67 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1.4 | 0 | 2 | 1 | 97 | 1 | 0.95 | 1 | 102 | 0 | 4 | 1 |
34 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0.035 | 1 | 2.5 | 1 | 119 | 1 | 1.55 | 1 | 76 | 0 | 4 | 1 |
70 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 2.5 | 1 | 2.3 | 1 | 84 | 1 | 0.92 | 1 | 92 | 0 | 4 | 1 |
78 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0.5 | 1 | 1.9 | 1 | 81 | 1 | 0.83 | 1 | 98 | 0 | 3 | 1 |
37 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1.7 | 1 | 1.9 | 1 | 95 | 1 | 1.05 | 1 | 90 | 0 | 3 | 1 |
85 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 7.3 | 1 | 2.4 | 1 | 66 | 1 | 1.09 | 1 | 61 | 0 | 4 | 1 |
25 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 1.1 | 0 | 2 | 1 | 101 | 1 | 1.07 | 1 | 94 | 0 | 4 | 1 |
26 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 1.8 | 1 | 2.5 | 1 | 147 | 1 | 1.13 | 1 | 129 | 0 | 1 | 1 |
58 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0.26 | 1 | 2.7 | 1 | 120 | 1 | 1.27 | 1 | 95 | 0 | 4 | 1 |
51 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 2.8 | 0 | 2 | 1 | 69 | 1 | 0.76 | 1 | 91 | 0 | 4 | 1 |
64 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1.4 | 0 | 2 | 0 | 103 | 0 | 0.98 | 0 | 107 | 0 | 4 | 1 |
44 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 45 | 1 | 1.4 | 1 | 39 | 1 | 1.16 | 1 | 33 | 0 | 3 | 2 |
48 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 5.4 | 1 | 1.9 | 1 | 87 | 1 | 1 | 1 | 87 | 0 | 4 | 1 |
61 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0.99 | 1 | 1.5 | 1 | 63 | 1 | 0.56 | 1 | 113 | 0 | 3 | 1 |
35 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0.25 | 1 | 2 | 1 | 121 | 1 | 0.81 | 1 | 148 | 0 | 4 | 1 |
70 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1.1 | 1 | 1.7 | 1 | 95 | 1 | 0.68 | 1 | 140 | 0 | 3 | 1 |
81 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0.2 | 1 | 2.2 | 1 | 133 | 1 | 0.78 | 1 | 171 | 0 | 4 | 1 |
83 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0.92 | 1 | 0.9 | 1 | 86 | 1 | 0.76 | 1 | 113 | 0 | 3 | 1 |
21 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1.4 | 1 | 1.8 | 1 | 163 | 1 | 1.05 | 1 | 155 | 0 | 4 | 1 |
87 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0.15 | 1 | 1.7 | 1 | 162 | 1 | 0.87 | 1 | 186 | 0 | 3 | 1 |
78 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0.64 | 1 | 1.7 | 1 | 113 | 1 | 0.99 | 1 | 115 | 0 | 3 | 1 |
64 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0.035 | 1 | 1 | 1 | 103 | 1 | 0.85 | 1 | 122 | 0 | 3 | 1 |
64 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 1 | 1.7 | 1 | 96 | 1 | 0.9 | 1 | 107 | 0 | 3 | 1 |
68 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0.4 | 1 | 2.2 | 1 | 117 | 1 | 0.86 | 1 | 136 | 0 | 3 | 1 |
44 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 2.5 | 1 | 1.6 | 1 | 119 | 1 | 1.09 | 1 | 110 | 0 | 4 | 1 |
53 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1.4 | 1 | 2.8 | 0 | 103 | 0 | 0.98 | 0 | 107 | 0 | 3 | 1 |
77 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 2 | 1 | 2.9 | 1 | 151 | 1 | 1.35 | 1 | 111 | 0 | 4 | 1 |
27 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 2.6 | 0 | 2 | 1 | 112 | 1 | 1.15 | 1 | 97 | 0 | 4 | 1 |
65 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 14.8 | 1 | 1.5 | 1 | 61 | 1 | 0.85 | 1 | 72 | 0 | 3 | 0 |
27 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 15 | 1 | 1.6 | 1 | 82 | 1 | 0.82 | 1 | 100 | 0 | 3 | 0 |
54 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 19 | 1 | 2.2 | 1 | 83 | 1 | 1.03 | 1 | 81 | 0 | 1 | 0 |
69 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1.8 | 1 | 2.3 | 1 | 97 | 1 | 0.89 | 1 | 109 | 0 | 3 | 1 |
42 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0.02 | 1 | 2.6 | 1 | 138 | 1 | 1.58 | 1 | 88 | 0 | 0 | 1 |
87 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 3 | 1 | 1.6 | 1 | 71 | 1 | 1.06 | 1 | 67 | 0 | 4 | 1 |
74 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 1 | 2.1 | 1 | 77 | 1 | 0.91 | 1 | 84 | 0 | 3 | 1 |
38 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 2.9 | 1 | 1.8 | 1 | 93 | 1 | 0.95 | 1 | 98 | 0 | 4 | 1 |
66 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1.3 | 1 | 1.7 | 1 | 86 | 1 | 0.91 | 1 | 94 | 0 | 4 | 1 |
69 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1.4 | 0 | 2 | 0 | 103 | 0 | 0.98 | 0 | 107 | 0 | 4 | 1 |
76 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 3.2 | 1 | 0.8 | 1 | 101 | 1 | 0.99 | 1 | 103 | 0 | 1 | 1 |
44 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0.2 | 1 | 1.5 | 1 | 107 | 1 | 0.79 | 1 | 135 | 0 | 4 | 1 |
45 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 9 | 1 | 2.8 | 1 | 237 | 1 | 1.17 | 1 | 203 | 0 | 0 | 1 |
36 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1.4 | 1 | 2 | 1 | 96 | 1 | 0.86 | 1 | 112 | 0 | 4 | 1 |
22 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1.3 | 0 | 2 | 1 | 110 | 1 | 0.94 | 1 | 117 | 0 | 4 | 1 |
59 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1.4 | 0 | 2 | 0 | 103 | 0 | 0.98 | 0 | 107 | 0 | 4 | 1 |
37 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1.6 | 0 | 2 | 1 | 67 | 1 | 0.71 | 1 | 95 | 0 | 4 | 1 |
65 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 4.3 | 0 | 2 | 1 | 88 | 1 | 0.72 | 1 | 122 | 0 | 3 | 1 |
70 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0.005 | 1 | 2.6 | 1 | 160 | 1 | 0.88 | 1 | 180 | 0 | 4 | 1 |
69 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0.31 | 0 | 2 | 1 | 118 | 1 | 0.83 | 1 | 142 | 0 | 4 | 1 |
66 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0.5 | 1 | 1.9 | 1 | 80 | 1 | 0.85 | 1 | 94 | 0 | 1 | 1 |
61 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0.61 | 1 | 1.6 | 1 | 103 | 1 | 0.93 | 1 | 111 | 0 | 3 | 1 |
44 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 2 | 1 | 1.3 | 1 | 136 | 1 | 0.94 | 1 | 145 | 0 | 2 | 1 |
43 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0.05 | 0 | 2 | 1 | 160 | 1 | 1.03 | 1 | 156 | 0 | 4 | 1 |
72 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1.4 | 0 | 2 | 1 | 114 | 1 | 1.11 | 1 | 102 | 0 | 4 | 1 |
82 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1.9 | 1 | 2.9 | 1 | 116 | 1 | 1.2 | 1 | 97 | 0 | 1 | 1 |
72 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 4.1 | 1 | 1.6 | 1 | 94 | 1 | 0.92 | 1 | 102 | 0 | 4 | 1 |
71 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0.8 | 1 | 2.3 | 1 | 133 | 1 | 1.1 | 1 | 121 | 0 | 3 | 1 |
34 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 7.8 | 1 | 2 | 1 | 95 | 1 | 0.99 | 1 | 96 | 0 | 0 | 0 |
31 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1.3 | 0 | 2 | 1 | 161 | 1 | 1.33 | 1 | 121 | 0 | 4 | 1 |
58 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0.25 | 1 | 2.4 | 1 | 102 | 1 | 0.77 | 1 | 134 | 0 | 4 | 1 |
39 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 160 | 1 | 0.4 | 1 | 11 | 1 | 1.24 | 1 | 8.9 | 0 | 4 | 2 |
49 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0.1 | 1 | 0.3 | 1 | 32 | 1 | 0.53 | 1 | 60 | 0 | 3 | 1 |
63 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0.025 | 1 | 1.6 | 1 | 124 | 1 | 0.89 | 1 | 139 | 0 | 4 | 1 |
80 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 2 | 1 | 1.2 | 1 | 39 | 1 | 0.95 | 1 | 41 | 0 | 3 | 1 |
26 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1.4 | 0 | 2 | 0 | 103 | 0 | 0.98 | 0 | 107 | 0 | 4 | 1 |
23 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1.3 | 1 | 2.2 | 1 | 103 | 1 | 1.35 | 1 | 76 | 0 | 4 | 1 |
39 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0.25 | 1 | 2.9 | 1 | 136 | 1 | 1.44 | 1 | 94 | 0 | 4 | 1 |
26 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 2 | 0 | 2 | 1 | 137 | 1 | 1.63 | 1 | 84 | 0 | 4 | 1 |
59 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1.5 | 0 | 2 | 1 | 92 | 1 | 0.93 | 1 | 99 | 0 | 3 | 1 |
39 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 4.3 | 1 | 2.4 | 1 | 135 | 1 | 1.51 | 1 | 89 | 0 | 4 | 1 |
69 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1.4 | 0 | 2 | 1 | 123 | 1 | 0.91 | 1 | 136 | 0 | 3 | 1 |
48 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0.3 | 1 | 1.6 | 1 | 86 | 1 | 0.93 | 1 | 92 | 0 | 3 | 1 |
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Check out the documentation for more information.
ThyroidAI: Multi-Modal Diagnostic System ThyroidAI is an end-to-end medical AI project that combines clinical tabular data with ultrasound imaging for robust thyroid disease classification.
🚀 Project Overview This system implements a 'Multi-Modal Brain' architecture that processes:
Clinical Markers: 28 features including TSH, T3, T4U, and patient history (Source: UCI Thyroid Dataset). Ultrasound Imaging: B-mode scans processed via a Custom CNN backbone (Source: DDTI Dataset). 🛠️ Key Components Unified Model (ThyroidAI.pth): A PyTorch ensemble model that fuses image embeddings with clinical feature vectors. Diagnostic Interface: An interactive Gradio UI for real-time risk assessment and clinical reporting. Data Augmentation: Synthetic expansion of clinical records to 11,316 rows for high-precision training. 📦 Datasets Used UCI Thyroid Disease: 3,772 primary records expanded to 11k+. DDTI (Digital Database Thyroid Images): 480 synchronized ultrasound scans. Thyroid-Diff-Clean: Fine-tuning dataset for differential diagnosis consistency. 💻 Installation & Usage Environment: Designed for Google Colab or environments with GPU support. Dependencies: torch, tensorflow, gradio, datasets, pandas, opencv-python. Run Interface: Execute the Gradio cell to launch the local or public shared URL. 🩺 Clinical Disclaimer This model is developed for research and educational purposes. All diagnostic outputs should be verified by a certified medical professional based on official TI-RADS standards.
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