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@article{190962,
        author = {SNEHA UNNARKAR and BIJAL PATEL},
        title = {Machine Learning Strategy for Ovarian Cancer Diagnosis},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {8},
        pages = {5057-5064},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=190962},
        abstract = {Ovarian cancer is one of the leading causes of cancer-death in female, if it is not detected at an early stage. Surgery, ancestry testing, ultrasound, CT-Scan, blood test like CA125 and extra medical examination are the main methods used to diagnose ovarian tumors whether it is benign or malignant in women. As timely detection of cancer is an important aspect, Machine learning is an emerging field that can make accurate projections by making inferences on data and may play a crucial role in Ovarian Cancer Prediction. According to observations, there are various Machine Learning Algorithms such as Support Vector Machine, K- nearest neighbor and Logistic regression etc. that may help to prevent cancer death if it is diagnosed at an early stage. The objective of the current study is to diagnose ovarian cancer accuracy using different Machine learning strategies.},
        keywords = {Ovarian Cancer, Early stage, Medical Examination, Machine learning algorithms.},
        month = {January},
        }