GitHub / hasansust32 / Prostate_Cancer_Predictio
His study addresses these concerns by predicting prostate cancer using six (6) machine learningtechniques: Random Forest, SVM, KNN, Logistic Regression, Neutral Network, and the Ensemble model. We gathered data from 100 patients who were placed in ten different circumstances. The data was categorised as malignant or non-cancerous. Among the six machine learning techniques, logistic regression, neuralnetworks, and ensemble learning have the potential to reach an accuracy of 95.00 percent. Ensemble learning can detect 96.55%of true positive prostate cancer in our model. KNN has a 90%accuracy rate, whereas SVM and Random Forest have an 85%accuracy rate.
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PURL: pkg:github/hasansust32/Prostate_Cancer_Predictio
Stars: 3
Forks: 0
Open issues: 0
License: None
Language: Jupyter Notebook
Size: 2 MB
Dependencies parsed at: Pending
Created at: about 4 years ago
Updated at: 8 months ago
Pushed at: 8 months ago
Last synced at: 4 months ago
Topics: cancer-detection, cancer-research, healthcare, machinelearning-python, prostate-cancer, prostate-cancer-biopsies, prostate-cancer-detection