Addition of new project under Machine Learning called as "Thyroid Disease Prediction Model with Feature Selection" #1203
Description
The "Thyroid Disease Detection using Machine Learning" project aims to develop a robust, efficient, and accurate system for predicting the presence of thyroid disorders in individuals. The solution leverages machine learning algorithms to analyze clinical and diagnostic data, identifying patterns indicative of thyroid dysfunction. By incorporating advanced data preprocessing, feature selection techniques, and classification models, the project seeks to enable early diagnosis and facilitate timely medical interventions, ultimately improving patient outcomes and reducing healthcare burdens.
Describe the solution you'd like:
I would like to add thyroid detection model that will include the following :
Data Preprocessing and Exploratory Data Analysis (EDA):
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Handle missing values, outliers, and imbalanced data.
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Perform statistical analysis and visualization of the dataset.
Feature Selection Techniques:
- Experiment with multiple feature selection methods, including PCA, Chi-Square, ANOVA, Mutual Information, and RFECV, to identify the most relevant features.
Classification Models:
- Train and evaluate models such as Logistic Regression, Random Forest, Decision Trees, and SVM to find the optimal classifier.
Performance Evaluation:
- Use metrics like accuracy, precision, recall, F1-score, and AUC-ROC to compare models
Additional context:
I will make use of kaggle dataset :https://www.kaggle.com/datasets/jainaru/thyroid-disease-data.
Please assign this to me under swoc25 label