In this project you are given a dataset and an article that uses this dataset. The authors have developed ten ML models for predicting survival of patients with heart failure and compared their performance. You must read the article to understand the problem, the dataset, and the methodology to complete the following tasks.
The dataset contains the medical records of patients who had heart failure, collected during their follow-up period.
Each patient profile has 13 clinical features. A detailed description of the dataset can be found in the Datasetsection of the provided article (patient_survival_prediction.pdf).
1. Read the article and reproduce the results presented in Table-4 using Python modules and packages (including your own script or customised codes). Write a report summarising the dataset, used ML methods, experiment protocol and results including variations, if any. During reproducing the results: (10 marks)
i) you should use the same set of features used by the authors.
ii) you should use the same classifier with exact parameter values.
iii) you should use the same training/test splitting approach as used by the authors.
iv) you should use the same pre/post processing, if any, used by the authors.
v) you should report the same performance metrics as shown in Table-4.
(i) Some of the ML methods are not covered in the current unit. Consider them as HD tasks i.e., based on the knowledge gained in the unit you should be able to find necessary packages and modules to reproduce the results.
(ii) If you find any issue in reproducing results or some subtle variations are found due to implementation differences of packages and modules in Python then appropriate explanation of them will be considered during evaluation of your submission.
(iii) Similarly, variation in results due to randomness of data splitting will also be considered during evaluation based on your explanation.
(iii) Obtained marks will be proportional to the number of ML methods that you will report in your submission with correctly reproduced results.
(iv) Make sure your Python code segment generates the reported results, otherwise you will receive zero marks for this task.
2. Design and develop your own ML solution for this problem. The proposed solution should be different from all approaches mentioned in the provided article. This does not mean that you must have to choose a new ML algorithm. You can develop a novel solution by changing the feature selection approach or parameter optimisations process of used ML methods or using different ML methods or different combinations of them.
This means, the proposed system should be substantially different from the methods presented in the article but not limited to only change of ML methods. Compare the result with reported methods in the article. Write a technical report summarising your solution design and outcomes. The report should include: (20 marks)
i) Motivation behind the proposed solution.
ii) How the proposed solution is different from existing ones.
iii) Detail description of the model including all parameters so that any reader can implement your model.
iv) Description of experimental protocol.
v) Evaluation metrics.
vi) Present results using tables and graphs.
vii) Compare and discuss results with respect to existing literatures.
viii) Appropriate references (IEEE numbered).
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