Increasing the Prediction Accuracy for Thyroid Disease: A Step Towards Better Health for Society

A healthy life is essential for a happy society, however it is a fact that seemingly invisible diseases plague our families and people suffer. The thyroid disease falls in such a category. Thyroid disorders are long-term and with carefully handled illnesses, people with thyroid disorders may also live stable and normal lives. Thyroid diagnosis, particularly for an inexperienced clinician, is a difficult proposal. Many researchers have established various methods for the diagnosis of the disease and several models for disease prediction have been developed. As with several other domains, machine learning approaches to modelling health care problems is gaining popularity. This study aims at providing solutions towards such a thyroid disease prediction. Dimension reduction techniques are applied, and reduced dimension data input to classifiers. Also, data augmentation is applied so as to be able to generate sufficient data for deep neural network model. Classifier prediction is compared to other similar researches. Real life dataset for thyroid disease has been used, and experiments conducted in distributed environment. Our proposed two stage approach gives a maximum accuracy of 99.95% which is very good as compared to existing techniques. We have shown that dimension reduction and data augmentation can be used very efficiently for achieving high accuracy of disease prediction.

Automated summary

The study aims at providing solutions for thyroid disease prediction using dimension reduction and data augmentation techniques, achieving a maximum accuracy of 99.95%. Experimental results demonstrate that these techniques can efficiently improve the accuracy of disease prediction.