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ThyroCare-Supervised-Machine-Learning-Model

Supervised Machine Learning Model for Thyroid Disease Classification

Thyroid Diseases Dataset

These reports were available at https://rb.gy/jbazib. These data were constructed containing the following:

  1. Age
  2. Sex
  3. On_thyroxine
  4. Query_on_thyroxine
  5. On_antithyroid_medi cation
  6. Sick
  7. Pregnant
  8. Thyroid_surgery
  9. l131_treatment
  10. Query_hypothyroid
  11. Query_hyperthyroid
  12. Lithium
  13. Goiter
  14. Tumor
  15. Hypopituitary
  16. Psych
  17. TSH_measured
  18. TSH
  19. T3_measured
  20. T3
  21. TT4_measured
  22. TT4
  23. T4U_measured
  24. T4U
  25. FTI_measured
  26. FTI
  27. TBG_measured
  28. TBG
  29. Referral_source
  30. Target
  31. ID

Data Preprocessing

The cleaned data of different thyroid diseases (hyperthyroidism, hypothyroidism and euthyroid) underwent synthetic data generation using MOSTLY AI which pioneered the generation of synthetic data for the development of AI models and software testing. Exploratory Data Analysis was carried out to see the correlation and significant relationship between the variables. These data were inspected and encoded in csv files. Then, it underwent preprocessing using python, NumPy and pandas to get an overview of the data types, fill null values, label categorical values, and remove outliers and

Training and Testing the ML Algorithms

Following data preprocessing, the dataset was split for the modeling process using the Sci-Kit Learn Library. The dataset was divided into eighty percent (80%) training sets and twenty percent (20%) validation sets.