Cancer Detection and Analysis Using Machine Learning

Verma, Abhishek; Shah, Cabinet Kumar; Kaur, Veerpal; Shah, Senate; Kumar, Prashant

doi:10.1109/iccsea54677.2022.9936457

Cited by 10 publications

(1 citation statement)

References 8 publications

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“…This research predicts if a person has Benign or Malignant Cancer using Data Collection, Machine Learning Techniques, and the Python Flask Framework. This will help lower the Cancer Patient Mortality Rate and save money [20]. This study reveals that artificial intelligence (A.I.)…”

Section: Literature Reviewmentioning

confidence: 86%

Prostate Cancer Detection and Analysis using Advanced Machine Learning

Alzboon,

Al-Batah

2023

IJACSA

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Prostate cancer is one of the leading causes of cancer-related deaths among men. Early detection of prostate cancer is essential in improving the survival rate of patients. This study aimed to develop a machine-learning model for detecting and diagnosing prostate cancer using clinical and radiological data. The dataset consists of 200 patients with prostate cancer and 200 healthy controls and extracted features from their clinical and radiological data. Then, the data trained and evaluated using several machines learning models, including logistic Regression, decision tree, random forest, support vector machine, and neural network models, using 10-fold crossvalidation. Our results show that the random forest model achieved the highest accuracy of 0.92, with a sensitivity of 0.95 and a specificity of 0.89. The decision tree model achieved a nearly similar accuracy of 0.91, while the logistic regression, support vector machine, and neural network models achieved lower accuracies of 0.86, 0.87, and 0.88, respectively. Our findings suggest that machine learning models can effectively detect and diagnose prostate cancer using clinical and radiological data. The random forest model may be the most suitable model for this task.

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Section: Literature Reviewmentioning

confidence: 86%