Separation of Different Blogs from Skin Disease Data using Artificial Intelligence.

Area of Science:

• Environmental Health
• Medical Informatics
• Machine Learning

Background:

• Skin diseases pose a global health risk, necessitating accurate prediction models.
• Healthcare data is abundant but often unstructured, posing challenges for analysis.
• Machine learning is increasingly used to understand disease-influencing variables.

Purpose of the Study:

• To develop an intelligent algorithm for effective feature identification and selection in predictive models.
• To enhance the accuracy and performance of skin disease prediction models.
• To address data preprocessing challenges, particularly missing data and unstructured formats.

Main Methods:

• Utilized artificial intelligence techniques including Support Vector Machines (SVM), decision trees, and logistic regression.
• Employed three distinct feature combination methodologies for model development.
• Focused on identifying relevant features and eliminating nonsignificant attributes.

Main Results:

• Achieved a tenfold increase in accuracy, F-measure, and precision for the models.
• Successfully identified and weighted the most important features for disease prediction.
• Demonstrated the effectiveness of the proposed intelligent algorithm in improving model performance.

Conclusions:

• The developed intelligent algorithm significantly enhances skin disease prediction models.
• Feature selection is crucial for improving the performance of machine learning models in healthcare.
• The methodology provides a robust approach to handling complex health data for better forecasting.