Comparative Analysis of Breast Cancer Detection using Cutting-edge Machine Learning Algorithms (MLAs)

  • Tanzeel Sultan Rana Octans Digital Pvt Ltd. 92-cc,Ex-Park View, Lahore ,Pakistan
  • Imran Saleem University of Management and Technology Lahore
  • Rabia Naseer Rao University of Management and Technology Lahore
  • Maryam Shabbir Department of Computer Sciences, Bahria University, Lahore, Pakistan
  • Laiba Wahid Chaudhry University of Management and Technology Lahore
Keywords: Decision Tree (DT), K-Nearest Neighbor (KNN), Logistic Regression (LR), Support Vector Machine (SVM), Machine Learning (ML), Multilayer Perceptron (MP), Random Forest (RF)


Abstract Views: 52

Recently, machine learning techniques have gained popularity for the medical diagnosis. Medical professionals use this approach to learn and detect the abnormalities of life-threatening chronic diseases. The increasing use of ML approaches may be due in part to better disease diagnosis enabled through improved symptom detection. The current study deployed different machine learning algorithms, including Decision Trees (DT), K-Nearest Neighbors (KNN), classifiers Multilayer Perceptron (MP), Support Vector Machines (SVM), and Random Forest (RF) for early predictions and symptoms of the disease. These models were  capable of differentiating between benign and harmful cancer cells Benign tumours, which were non-cancerous and in most cases, non-lethal  were mostly confined to the area from where they originated, however, it was observed that malignant cancer can start with abnormal cell growth in the human body. This abnormal cell growth can quickly spread to nearby tissues, which can cause infiltration of adjacent cells, resulting in a potentially fatal condition. Thereby, it was observed that Multilayer Perceptron (MLP) model provided the highest accuracy percentage of 86% when compared with all the other techniques in association with the accuracy rate of the models


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How to Cite
Rana, T. S., Saleem, I., Rao, R. N., Shabbir, M., & Chaudhry, L. W. (2023). Comparative Analysis of Breast Cancer Detection using Cutting-edge Machine Learning Algorithms (MLAs). Innovative Computing Review, 3(1).