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Breast cancer poses a global threat to women’s health, influencing its danger level through factors like tumor characteristics and disease stage. In 2022, 2.3 million women were affected, resulting in 685,000 deaths. Early detection is vital, and recent advancements in AI and soft computing have transformed breast cancer diagnosis. AI’s capacity to analyze extensive datasets enhances accuracy reduces human error, ensures consistent interpretation, and allows for timely interventions, improving treatment outcomes. Similarly, thyroid disease detection has advanced, with AI and soft computing showing promise. Our paper proposes a statistical and soft computing approach for thyroid and breast cancer diagnosis, combining clinical and medical data to train a model. Testing on a sizable dataset indicates the potential to revolutionize early-stage diagnosis, prompting timely treatment, and improving survival rates. The suggested technique reliably forecasts thyroid and breast cancer, aiding medical practitioners in recognizing high-risk patients and tailoring efficient disease management strategies through machine learning.

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Human Disease Prediction and Feature Optimization System Using Machine Learning Algorithm and Soft Computing Approach

  • 会议论文

  • 发表日期：

    2025年

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  • Madhulika Gautam,
  • Kadambri Agarwal,
  • Shweta Roy

摘要

Breast cancer poses a global threat to women’s health, influencing its danger level through factors like tumor characteristics and disease stage. In 2022, 2.3 million women were affected, resulting in 685,000 deaths. Early detection is vital, and recent advancements in AI and soft computing have transformed breast cancer diagnosis. AI’s capacity to analyze extensive datasets enhances accuracy reduces human error, ensures consistent interpretation, and allows for timely interventions, improving treatment outcomes. Similarly, thyroid disease detection has advanced, with AI and soft computing showing promise. Our paper proposes a statistical and soft computing approach for thyroid and breast cancer diagnosis, combining clinical and medical data to train a model. Testing on a sizable dataset indicates the potential to revolutionize early-stage diagnosis, prompting timely treatment, and improving survival rates. The suggested technique reliably forecasts thyroid and breast cancer, aiding medical practitioners in recognizing high-risk patients and tailoring efficient disease management strategies through machine learning.