Breast Cancer Detection and Prevention Using Machine Learning Assignment

Assignment Task

Aim and Objectives

The main aim of this research is to propose a model to predict the occurrence of breast cancer based on their risk factors. The identification of the breast cancer incidence using the well- studied risk factors allows for a quick and cost-effective diagnosis and the recurrence of this disease can also be predicted based on the disease model generated.
The research objectives are formulated based on the aim of this study, which are as follows:

• To analyze the pattern and relationship between the risk factors of breast cancer via visualization to improve the comprehensibility of diagnosis for clinicians and patients.
• To suggest a suitable balancing technique that can be applied on the imbalanced dataset.
• To compare between the predictive models to identify the most accurate model to
  classify breast cancer occurrence based on its risk factors.
• To evaluate the performance of the classifiers based on the balancing techniques.

The main aim of this research is to develop a personalised blood glucose prediction model using only non-CGM data. The goal of this research is to contribute to the vast majority of diabetic patients that do not use CGM for self-monitoring of blood glucose levels.

• The research objectives are formulated based on the aim of this study, which are as follows:
• To investigate the performance of existing blood glucose prediction models developed using non-CGM data
• To develop a personalised prediction model using only non-CGM data
  To evaluate the performance of the proposed blood glucose prediction model

The aim of this research is to propose an approach to enhance the projecting capability of the Lee-Carter model and fit the model to the Mauritian mortality data from 1984 to 20181. The goal of this study is to forecast the mortality rate of Mauritius and provide solutions to insurance companies and pension providers to alleviate the effects of ageing population.

• The objectives of the research are outlined as follows.
• To investigate state-of-the-art approaches to the Lee-Carter model used in modelling and forecasting mortality rate.
• To determine the optimum technique to estimate the parameters of the Lee-Carter model.
• To propose a deep-learning model to forecast the mortality index parameter.
• To evaluate the performance of the Lee-Carter model.

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