FELICITATION OF MEDICAL EXPERTISE IN CANCER THROUGH MACHINE LEARNING MODELS WITH KNOWLEDGE DATA DISCOVERY (KDD)

Rashid Hussain; Aminu Abdullahi; Baffa Sani Mahmoud

Authors

Rashid Hussain Department Computer Science, Sule Lamido University Kafin Hausa, Jigawa State, Nigeria
Aminu Abdullahi Chief Medical Officer, Sule Lamido University Kafin Hausa, Jigawa State, Nigeria
Baffa Sani Mahmoud Department Computer Science, Sule Lamido University Kafin Hausa, Jigawa State, Nigeria

Keywords:

Breast Cancer, Supervised Learning, Classification, Roc-Auc, Interpretability, Machine Learning

Abstract [English]

Cancer remains one of the leading causes of mortality worldwide, posing a significant challenge to modern healthcare systems. Among various types, breast cancer stands out as a major concern. This paper specifically focuses on breast cancer. Breast cancer remains the most diagnosed malignancy among women worldwide and early detection is critical to improve patient outcomes. This research paper presents a supervised machine learning evaluation for breast cancer diagnosis using a clinical features dataset (569 samples, 30 numeric features). After processing of the dataset, comparisons were made with five supervised classifiers such as Logistic Regression, Decision Tree, Random Forest (RF), Support Vector Machine (SVM), and K Nearest Neighbors (K-NN). Evaluations have been done of these models on these parameters such as accuracy, precision, recall, F1 score, and ROC-AUC on a stratified test split. Among all, logistic regression achieved the highest ROC-AUC, which is 99.6% and overall accuracy was 97% on the test set, closely followed by SVM and Random Forest. Further, this paper discussed model interpretability, robustness, clinical implications and future scope of improvement.

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