MACHINE LEARNING ALGORITHM USING CLASSIFICATION AND REGRESSION TREES, LINEAR GAUSSIAN NAIVE BAYES, SUPPORT VECTOR MACHINES, CART AND K-NEAREST NEIGHBORS TO AID CANCER RESEARCH FIRMS IN DATA GROUPING AND BETTER ANALYSIS.

Rebeccah Ndungi, NIM.: 21206051022 (2023) MACHINE LEARNING ALGORITHM USING CLASSIFICATION AND REGRESSION TREES, LINEAR GAUSSIAN NAIVE BAYES, SUPPORT VECTOR MACHINES, CART AND K-NEAREST NEIGHBORS TO AID CANCER RESEARCH FIRMS IN DATA GROUPING AND BETTER ANALYSIS. Masters thesis, UIN SUNAN KALIJAGA YOGYAKARTA.

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Abstract

World Health Organization publications disclose that breast cancer is one of the most common diseases amongst the women, and that it has a high death rate. Its prevalence is growing in developing nations, where the vast majority of cases are discovered late in the disease’s development. Preventing this malignancy may be accomplished with safeguards and frequent examinations. Aside from that, early detection of the illness may be beneficial in terms of battling the condition. This deep learning algorithm is a strong approach for identifying features about the breast mass that are often not visible to the human eye through deep learning analysis of the mammogram. The algorithm will then be evaluated through various metrics to validate on its accuracy. Deep learning is a technology that will be used in this project. The algorithm intends to help cancer firm research centres in Kenya and globally in quicker grouping, identification and aid doctors in quicker diagnosis and research.

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