• Thyroid disease is one of the most common disease with endocrine disorders in the human population today. For example, hyperthyroidism(over) and hypothyroidism(under), which are relate to release of amount of thyroid hormones the thyroid gland produces and whether it is overactiveTrusted Source (when the thyroid gland makes too much thyroid hormone) or underactiveTrusted Source (when the thyroid gland doesn't make enough thyroid hormone). To solve this problem, to do effective classification and for better prediction we will use Machine Learning algorithms.

• Given features are combiningly/relatively give us better result for the disease. So we will apply ensemble techniques.

• The main objective is to develop a system which can predict the type of thyroid disease that patient is affected from.

• To predict thyroid disease with usage of minimumnumber of parameters.

• To predict all possible types of Thyroid diseases.

• From Garavan Institute
• Documentation: as given by Ross Quinlan
• 6 databases from the Garavan Institute in Sydney, Australia

• Python Version: 3.9

• Packages: pandas, numpy, matplotlib, seaborn, sklearn.

• Requirements:

  • !pip install pandas

  • !pip install numpy

  • !pip install matplotlib

  • !pip install seaborn

  • !pip install scikit-learn

See the following for a discussion of relevant experiments and related work:

Quinlan,J.R., Compton,P.J., Horn,K.A., & Lazurus,L. (1986).

Inductive knowledge acquisition: A case study.

In Proceedings of the Second Australian Conference on Applications of Expert Systems. Sydney, Australia.

Quinlan,J.R. (1986). Induction of decision trees. Machine Learning, 1, 81--106.