A comprehensive analysis using neural network-based model for thyroid disease prediction

Nowadays data analysis has an important role in building a machine-learning model, especially in the case of medical data analysis. Statistical analysis tools help us to analyze large amounts of data and are also used to identify common trends and patterns in the dataset. This statistical analysis can be used to convert big data into meaningful information. In the case of medical datasets, the major issue is inconsistent data representation, for example, after diagnosis, some medical experts will represent the gender as F and M for male and female some others will represent it as 1 and 0, and sometimes the same expert will use a different format for the same gender representation, so the data pre-processing has an important role here. For the statistical analysis of the medical dataset, some python tools are used. Here thyroid medical datasets are used for the statistical analysis. After statistical analysis, this dataset is passed over to a deep learning neural network and got an accuracy of 99.07%, F1-score of 93.69%, Recall of 89.66%, and Precision of 98.11%.