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Age int64 | Gender int64 | Smoking int64 | Hx Smoking int64 | Hx Radiothreapy int64 | Thyroid Function int64 | Physical Examination int64 | Adenopathy int64 | Pathology int64 | Focality int64 | Risk int64 | T int64 | N int64 | M int64 | Stage int64 | Response int64 | Recurred int64 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
59 | 1 | 1 | 1 | 0 | 1 | 1 | 3 | 1 | 0 | 1 | 4 | 0 | 0 | 0 | 3 | 1 |
80 | 0 | 1 | 0 | 0 | 2 | 4 | 5 | 3 | 0 | 1 | 3 | 2 | 0 | 1 | 3 | 1 |
60 | 0 | 0 | 0 | 0 | 2 | 4 | 3 | 3 | 1 | 2 | 2 | 0 | 0 | 0 | 0 | 0 |
29 | 0 | 0 | 0 | 0 | 2 | 1 | 3 | 0 | 1 | 2 | 1 | 0 | 0 | 0 | 1 | 0 |
34 | 0 | 0 | 0 | 0 | 2 | 1 | 0 | 3 | 1 | 1 | 3 | 2 | 0 | 0 | 2 | 0 |
38 | 0 | 0 | 0 | 0 | 2 | 4 | 5 | 3 | 0 | 1 | 2 | 2 | 0 | 0 | 1 | 0 |
28 | 0 | 0 | 0 | 0 | 2 | 4 | 3 | 3 | 1 | 2 | 2 | 0 | 0 | 0 | 2 | 0 |
38 | 0 | 0 | 0 | 0 | 0 | 0 | 3 | 2 | 1 | 2 | 0 | 0 | 0 | 0 | 1 | 0 |
29 | 0 | 0 | 0 | 0 | 2 | 1 | 5 | 3 | 0 | 1 | 3 | 2 | 0 | 0 | 3 | 1 |
24 | 0 | 0 | 0 | 0 | 2 | 4 | 3 | 3 | 1 | 2 | 2 | 0 | 0 | 0 | 1 | 0 |
63 | 1 | 1 | 0 | 0 | 2 | 4 | 5 | 3 | 0 | 1 | 3 | 2 | 0 | 1 | 3 | 1 |
60 | 0 | 0 | 0 | 0 | 2 | 1 | 0 | 3 | 0 | 1 | 2 | 2 | 0 | 0 | 3 | 1 |
35 | 0 | 0 | 0 | 0 | 2 | 1 | 5 | 3 | 0 | 1 | 1 | 2 | 0 | 0 | 3 | 1 |
58 | 0 | 0 | 0 | 0 | 4 | 1 | 1 | 3 | 0 | 1 | 4 | 2 | 0 | 1 | 3 | 1 |
20 | 0 | 0 | 0 | 0 | 2 | 1 | 3 | 3 | 1 | 1 | 2 | 1 | 0 | 0 | 1 | 0 |
32 | 1 | 0 | 1 | 0 | 2 | 1 | 0 | 3 | 0 | 1 | 4 | 2 | 0 | 0 | 3 | 1 |
38 | 0 | 0 | 0 | 0 | 2 | 3 | 4 | 3 | 0 | 0 | 3 | 2 | 1 | 1 | 3 | 1 |
33 | 1 | 0 | 0 | 0 | 2 | 4 | 3 | 3 | 1 | 2 | 2 | 2 | 0 | 0 | 1 | 0 |
63 | 0 | 0 | 0 | 0 | 2 | 4 | 3 | 0 | 1 | 1 | 3 | 0 | 0 | 1 | 2 | 1 |
35 | 0 | 0 | 0 | 0 | 2 | 1 | 3 | 3 | 0 | 2 | 2 | 0 | 0 | 0 | 1 | 0 |
32 | 0 | 0 | 0 | 0 | 2 | 1 | 3 | 1 | 0 | 2 | 3 | 0 | 0 | 0 | 1 | 0 |
73 | 0 | 0 | 0 | 0 | 2 | 4 | 3 | 3 | 1 | 2 | 1 | 0 | 0 | 0 | 1 | 0 |
31 | 0 | 0 | 0 | 0 | 2 | 1 | 0 | 3 | 0 | 1 | 3 | 2 | 0 | 0 | 3 | 1 |
33 | 0 | 0 | 0 | 0 | 2 | 1 | 3 | 2 | 1 | 2 | 0 | 0 | 0 | 0 | 1 | 0 |
70 | 0 | 0 | 0 | 0 | 2 | 3 | 3 | 3 | 0 | 2 | 3 | 0 | 0 | 0 | 2 | 0 |
62 | 0 | 0 | 0 | 0 | 1 | 1 | 3 | 3 | 1 | 2 | 2 | 0 | 0 | 0 | 0 | 1 |
35 | 0 | 0 | 0 | 0 | 2 | 4 | 3 | 3 | 1 | 2 | 2 | 0 | 0 | 0 | 1 | 0 |
42 | 1 | 0 | 0 | 0 | 4 | 4 | 3 | 3 | 0 | 1 | 3 | 1 | 0 | 0 | 2 | 0 |
24 | 0 | 0 | 0 | 0 | 2 | 4 | 3 | 0 | 1 | 2 | 2 | 0 | 0 | 0 | 1 | 0 |
41 | 0 | 0 | 0 | 0 | 2 | 4 | 3 | 2 | 1 | 2 | 0 | 0 | 0 | 0 | 1 | 0 |
30 | 0 | 0 | 0 | 0 | 2 | 1 | 5 | 3 | 1 | 1 | 3 | 2 | 0 | 0 | 3 | 1 |
34 | 0 | 0 | 0 | 0 | 2 | 1 | 5 | 3 | 1 | 0 | 5 | 2 | 0 | 0 | 3 | 1 |
27 | 0 | 0 | 0 | 0 | 2 | 1 | 5 | 3 | 0 | 1 | 3 | 2 | 0 | 0 | 1 | 0 |
27 | 0 | 0 | 0 | 0 | 1 | 4 | 3 | 3 | 1 | 2 | 2 | 0 | 0 | 0 | 1 | 0 |
63 | 0 | 0 | 0 | 0 | 2 | 3 | 3 | 3 | 1 | 2 | 2 | 0 | 0 | 0 | 1 | 0 |
34 | 0 | 0 | 0 | 0 | 2 | 4 | 5 | 3 | 1 | 1 | 2 | 2 | 0 | 0 | 3 | 1 |
51 | 0 | 0 | 0 | 0 | 2 | 4 | 3 | 2 | 1 | 2 | 0 | 0 | 0 | 0 | 1 | 0 |
31 | 0 | 0 | 0 | 0 | 2 | 3 | 2 | 3 | 1 | 1 | 4 | 2 | 0 | 0 | 1 | 1 |
31 | 0 | 0 | 0 | 0 | 1 | 1 | 3 | 2 | 1 | 1 | 0 | 0 | 0 | 0 | 1 | 0 |
67 | 0 | 0 | 0 | 0 | 2 | 3 | 3 | 3 | 1 | 2 | 2 | 0 | 0 | 0 | 1 | 0 |
27 | 0 | 0 | 0 | 0 | 2 | 1 | 3 | 3 | 1 | 2 | 1 | 0 | 0 | 0 | 1 | 0 |
24 | 0 | 0 | 0 | 0 | 2 | 3 | 3 | 3 | 1 | 2 | 2 | 0 | 0 | 0 | 1 | 0 |
29 | 0 | 1 | 0 | 0 | 2 | 3 | 3 | 0 | 1 | 2 | 2 | 0 | 0 | 0 | 3 | 1 |
27 | 0 | 0 | 0 | 0 | 2 | 4 | 3 | 3 | 1 | 2 | 2 | 0 | 0 | 0 | 1 | 0 |
28 | 0 | 1 | 0 | 0 | 0 | 4 | 3 | 3 | 1 | 2 | 2 | 1 | 0 | 0 | 2 | 0 |
48 | 0 | 0 | 0 | 0 | 0 | 1 | 2 | 1 | 0 | 1 | 4 | 1 | 0 | 0 | 3 | 0 |
30 | 0 | 0 | 0 | 0 | 2 | 4 | 3 | 2 | 1 | 2 | 0 | 0 | 0 | 0 | 1 | 0 |
24 | 0 | 0 | 0 | 0 | 2 | 3 | 3 | 3 | 1 | 2 | 2 | 0 | 0 | 0 | 0 | 0 |
36 | 0 | 0 | 0 | 0 | 2 | 4 | 3 | 3 | 1 | 2 | 2 | 0 | 0 | 0 | 1 | 0 |
37 | 0 | 0 | 0 | 0 | 4 | 3 | 3 | 2 | 1 | 2 | 0 | 0 | 0 | 0 | 1 | 0 |
31 | 0 | 0 | 0 | 0 | 2 | 3 | 3 | 0 | 1 | 1 | 3 | 1 | 0 | 0 | 0 | 1 |
55 | 1 | 0 | 0 | 0 | 2 | 1 | 3 | 3 | 0 | 1 | 3 | 0 | 0 | 1 | 2 | 0 |
43 | 0 | 0 | 0 | 0 | 2 | 1 | 3 | 2 | 1 | 2 | 0 | 0 | 0 | 0 | 1 | 0 |
50 | 0 | 0 | 0 | 0 | 2 | 4 | 3 | 3 | 1 | 2 | 2 | 0 | 0 | 0 | 1 | 0 |
63 | 0 | 0 | 0 | 0 | 2 | 2 | 3 | 3 | 1 | 2 | 2 | 0 | 0 | 0 | 0 | 0 |
70 | 0 | 0 | 0 | 0 | 2 | 4 | 3 | 2 | 1 | 2 | 0 | 0 | 0 | 0 | 1 | 0 |
51 | 0 | 0 | 0 | 0 | 2 | 1 | 3 | 3 | 0 | 0 | 5 | 1 | 1 | 1 | 3 | 1 |
27 | 0 | 0 | 0 | 0 | 2 | 4 | 3 | 0 | 1 | 2 | 2 | 0 | 0 | 0 | 1 | 0 |
62 | 1 | 1 | 0 | 0 | 2 | 3 | 2 | 3 | 0 | 0 | 5 | 2 | 1 | 4 | 3 | 1 |
42 | 0 | 0 | 0 | 0 | 2 | 4 | 3 | 2 | 1 | 2 | 0 | 0 | 0 | 0 | 2 | 0 |
36 | 0 | 0 | 0 | 0 | 2 | 4 | 5 | 3 | 1 | 2 | 2 | 2 | 0 | 0 | 2 | 0 |
25 | 0 | 0 | 0 | 0 | 2 | 1 | 3 | 3 | 1 | 2 | 2 | 0 | 0 | 0 | 2 | 0 |
21 | 0 | 0 | 0 | 0 | 2 | 4 | 3 | 3 | 0 | 1 | 4 | 0 | 0 | 0 | 3 | 1 |
31 | 0 | 0 | 0 | 0 | 2 | 4 | 3 | 3 | 1 | 2 | 2 | 0 | 0 | 0 | 1 | 0 |
31 | 1 | 1 | 0 | 0 | 2 | 3 | 3 | 3 | 1 | 2 | 2 | 0 | 0 | 0 | 1 | 0 |
25 | 0 | 0 | 0 | 0 | 2 | 1 | 2 | 3 | 0 | 1 | 4 | 2 | 0 | 0 | 3 | 1 |
38 | 0 | 0 | 0 | 0 | 2 | 1 | 3 | 3 | 1 | 2 | 3 | 0 | 0 | 0 | 1 | 0 |
28 | 0 | 0 | 0 | 0 | 2 | 1 | 3 | 3 | 1 | 2 | 1 | 0 | 0 | 0 | 1 | 0 |
38 | 0 | 0 | 0 | 0 | 2 | 1 | 3 | 3 | 0 | 2 | 1 | 0 | 0 | 0 | 1 | 0 |
25 | 0 | 0 | 0 | 0 | 2 | 4 | 5 | 3 | 0 | 2 | 2 | 2 | 0 | 0 | 1 | 0 |
50 | 0 | 0 | 0 | 0 | 2 | 1 | 3 | 3 | 1 | 2 | 2 | 0 | 0 | 0 | 2 | 0 |
80 | 1 | 1 | 0 | 0 | 2 | 3 | 3 | 1 | 0 | 1 | 5 | 0 | 0 | 1 | 3 | 1 |
33 | 0 | 0 | 0 | 0 | 2 | 3 | 3 | 3 | 1 | 2 | 2 | 0 | 0 | 0 | 1 | 0 |
45 | 0 | 0 | 0 | 0 | 2 | 1 | 3 | 3 | 1 | 2 | 2 | 0 | 0 | 0 | 1 | 0 |
43 | 1 | 0 | 0 | 0 | 2 | 1 | 3 | 2 | 0 | 2 | 0 | 0 | 0 | 0 | 1 | 0 |
48 | 0 | 0 | 0 | 0 | 2 | 1 | 0 | 3 | 0 | 1 | 3 | 2 | 0 | 0 | 3 | 1 |
28 | 0 | 0 | 0 | 0 | 1 | 4 | 3 | 3 | 1 | 2 | 2 | 0 | 0 | 0 | 1 | 0 |
52 | 1 | 1 | 0 | 0 | 2 | 3 | 3 | 2 | 1 | 2 | 0 | 0 | 0 | 0 | 1 | 0 |
59 | 0 | 0 | 0 | 0 | 2 | 3 | 3 | 2 | 1 | 2 | 0 | 0 | 0 | 0 | 1 | 0 |
30 | 0 | 0 | 0 | 0 | 3 | 1 | 3 | 3 | 0 | 2 | 1 | 0 | 0 | 0 | 1 | 0 |
20 | 0 | 0 | 0 | 0 | 2 | 4 | 3 | 2 | 1 | 2 | 0 | 0 | 0 | 0 | 1 | 0 |
71 | 0 | 1 | 0 | 0 | 2 | 3 | 3 | 0 | 0 | 0 | 5 | 0 | 1 | 4 | 3 | 1 |
21 | 0 | 0 | 0 | 0 | 2 | 3 | 5 | 3 | 0 | 2 | 3 | 2 | 0 | 0 | 1 | 0 |
38 | 0 | 0 | 0 | 0 | 2 | 1 | 3 | 0 | 0 | 2 | 3 | 0 | 0 | 0 | 0 | 0 |
67 | 1 | 1 | 0 | 0 | 2 | 1 | 3 | 3 | 0 | 2 | 2 | 0 | 0 | 0 | 1 | 0 |
40 | 0 | 0 | 0 | 0 | 2 | 4 | 3 | 2 | 1 | 2 | 0 | 0 | 0 | 0 | 1 | 0 |
47 | 0 | 0 | 0 | 0 | 2 | 1 | 3 | 0 | 1 | 1 | 3 | 0 | 0 | 0 | 2 | 0 |
67 | 1 | 1 | 0 | 0 | 2 | 1 | 0 | 3 | 0 | 0 | 6 | 2 | 0 | 3 | 3 | 1 |
32 | 0 | 0 | 0 | 0 | 2 | 4 | 3 | 3 | 1 | 2 | 2 | 0 | 0 | 0 | 1 | 0 |
56 | 1 | 0 | 0 | 0 | 2 | 1 | 3 | 3 | 1 | 2 | 2 | 0 | 0 | 0 | 0 | 1 |
51 | 0 | 0 | 1 | 0 | 2 | 1 | 3 | 3 | 1 | 2 | 2 | 0 | 0 | 0 | 1 | 0 |
37 | 1 | 0 | 0 | 0 | 2 | 1 | 0 | 3 | 0 | 1 | 3 | 2 | 0 | 0 | 3 | 1 |
29 | 0 | 0 | 0 | 0 | 2 | 4 | 3 | 3 | 1 | 2 | 2 | 0 | 0 | 0 | 1 | 0 |
62 | 0 | 0 | 0 | 0 | 2 | 4 | 5 | 3 | 0 | 1 | 2 | 2 | 0 | 1 | 3 | 1 |
27 | 0 | 0 | 0 | 0 | 2 | 1 | 3 | 3 | 1 | 2 | 2 | 0 | 0 | 0 | 1 | 0 |
42 | 0 | 0 | 0 | 0 | 2 | 4 | 3 | 3 | 1 | 2 | 2 | 0 | 0 | 0 | 1 | 0 |
41 | 0 | 0 | 0 | 0 | 2 | 4 | 3 | 3 | 1 | 2 | 2 | 0 | 0 | 0 | 1 | 0 |
56 | 0 | 0 | 0 | 0 | 2 | 3 | 3 | 0 | 0 | 2 | 3 | 0 | 0 | 0 | 3 | 1 |
35 | 0 | 0 | 0 | 0 | 0 | 1 | 3 | 3 | 1 | 2 | 3 | 0 | 0 | 0 | 1 | 0 |
30 | 0 | 0 | 0 | 0 | 2 | 4 | 3 | 3 | 1 | 2 | 2 | 0 | 0 | 0 | 1 | 0 |
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Thyroid Diff Clean Dataset
Description
This dataset contains cleaned and preprocessed thyroid disease data prepared for machine learning tasks.
Categorical features are encoded and missing values have been handled.
The dataset includes a structured train–test split.
Dataset Structure
- train.csv — training split (80%)
- test.csv — testing split (20%)
Features
The dataset includes clinical and diagnostic features related to thyroid condition prediction.
The target variable represents recurrence status.
Preprocessing Steps
- Removed duplicate records
- Handled missing values
- Encoded categorical variables
- Stratified train–test split (80–20)
Intended Use
This dataset is suitable for:
- Classification tasks
- Medical machine learning experiments
- Educational projects
- Benchmarking tabular ML models
License
Apache-2.0
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