Abstract: In India, ovarian cancer is the third most frequent malignancy. Every year, it affects over a lakh people. In 2018, 295,414 women were diagnosed with ovarian cancer, and 184,799 women died from the disease globally, according to statistics. in their life. At some time in their life, one out of every 78 women will develop ovarian cancer. Because early-stage tumors are often asymptomatic, the vast majority of ovarian cancer patients are diagnosed with advanced disease. As a result, long-term survival appears to be improbable. To find out if you have this cancer, consult a doctor or travel to a diagnostic center, which can take time. A few statistical-based approaches to dealing with this problem are currently being explored, and they have become part of a partial answer to some extent. In the healthcare industry, machine learning has made a wide range of tools, methodologies, and frameworks available. Machine learning is the most effective connectionist technique for predicting cancer outcomes because it can identify and recognize patterns in complex datasets. Intending to lower mortality rates, this project provides a set of classification-based machine learning algorithms for cancer detection and prevention. Our goal is to create a simple predictive model that also performs well. It's done using classification techniques including Decision Tree (DT), Logistic Regression (LR), and Support Vector Machine (SVM) (SVM). The accuracy of each categorization method's conclusions is compared.
keywords: Ovarian cancer, Decision Tree (DT), Logistic Regression (LR), Support Vector Machine (SVM).
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DOI:
10.17148/IJARCCE.2022.11625
