Analysis of data mining techniques and algorithms for healthcare application using cervical cancer as a case study.
Date
2018-04
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Ashesi University
Abstract
Cervical cancer is the most common cause of cancer among African women. It is
a preventable disease and can be treated if identified at early stages. Given the lack of
adequate health care services and the costly nature of colposcopies in Africa, it is difficult
to get an early diagnosis. The development of smartphone-based diagnostic tools like
MobileODT – with which pictures of the cervix are taken and sent to doctors for diagnosis
– promises to address the expensive nature of colposcopy and Pap test; still, the diagnosis
of these images is prone to human errors. This project aimed to recommend an algorithm
that best classifies cervical images into cancerous and non-cancerous, in order to aid
medical officials to give a better diagnosis. K-Nearest Neighbour (KNN), Convolutional
Neural Network (CNN) and Support Vector Machine (SVM) were analyzed and compared based on their classification accuracy, sensitivity and specificity and how these results varied after applying Principal Component Analysis (PCA) on the dataset. KNN, CNN, and SVM models obtained classification accuracies of 68.75%, 83.3%, and 66.37% respectively while PCA-KNN and PCA-SVM models had classification accuracies of 78.12% and 62.7% respectively.
Description
Applied Thesis submitted to the Department of Computer Science, Ashesi University, in partial fulfillment of Bachelor of Science degree in Computer Science, April 2018
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Keywords
MobileODT, K-Nearest Neighbour (KNN), Convolutional Neural Network (CNN), Support Vector Machine (SVM), Cervical cancer, cancer among African women