A Comparative Analysis and Prediction of Ovarian Cancer using AI Approach


  • Sumit Das JIS College of Engineering, Information Technology, Kalyani, 741235, India
  • Dipansu Mondal University of Kalyani,CIRM, 741235, India
  • Priyanka Roy JIS College of Engineering, Information Technology, Kalyani, 741235, India
  • Tanusree Das JIS College of Engineering, Information Technology, Kalyani, 741235, India
  • Risha Roy JIS College of Engineering, Information Technology, Kalyani, 741235, India
  • Diprajyoti Majumdar JIS College of Engineering, Information Technology, Kalyani, 741235, India




Artificial Intelligence (AI), Ovarian Cancer, Decision Tree, ID3 Algorithm, Prolog


The aim of this paper is to analyze and predict ovarian cancer in women using Artificial Intelligence. The program in logic and the decision tree of machine learning are being created to presume Ovarian Cancer. Ovarian malignancy is a significant infection among ladies, even at a very early age. The side effects of ovarian diseases are taken as the factors to settle on the choice tree to foresee the conceivable outcomes. The fundamental side effects would be the foundations of the sickness to settle on the choice tree furthermore than all the yes and no of the tree would have a determination or an outcome. This will assist the women to aware of the type of the ovarian cancer with symptoms and to take necessary steps to avoid this deadly disease.   As per the research outcome, it is quite helpful for women all over the world to be aware of the disease. Analysis and prediction provide a major outcome of this research. Advanced technology helps move the health system in a new direction. It gives attention to ladies about ovarian malignancy from one side of the planet to the other. There are numerous country regions all around the world exists where the specialist and the patient proportion are poor, there it can furnish attention to ovarian malignancy alongside the expectation if any patient has ovarian disease or not. Any little or big indications of ovarian disease, they will become more acquainted with what sort of ovarian malignant growth they have through the product. It will decrease the mortality rate. 



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How to Cite

Das, S., Mondal, D. ., Roy, P. ., Das, T. ., Roy, R., & Majumdar, D. . (2023). A Comparative Analysis and Prediction of Ovarian Cancer using AI Approach. Asia-Pacific Journal of Management and Technology (AJMT), 3(3), 22-32. https://doi.org/10.46977/apjmt.2023.v03i03.003