One of the biggest challenges for us is predicting the risk of an event happening based on certain features. The task is to estimate the risk of death within 7 years, with the primary cause of death being circulatory.
This dataset contains measurements of serum free light chain for 7874 subjects[1].
The dataset has 9 features:
age: age in yearssex: F=female, M=malesample.yr: the calendar year in which a blood sample was obtainedkappa: serum free light chain, kappa portionlambda: serum free light chain, lambda portionflc.grp: the serum free light chain group for the subject, as used in the original analysiscreatinine: serum creatininemgus: whether the subject had been diagnosed with monoclonal gammapothy (MGUS)chapter: for those who died, a grouping of their primary cause of death by chapter headings of the International Code of Diseases ICD-9death: boolean indicating whether the subject died or the event time is right censoredfutime: total length of follow-up or time of death, in days
The endpoint is death, which occurred for 2169 subjects (27.5%).
We would like you to:
- Create a prototype model estimating the risk of death due to circulatory causes within 7 years, using python
- Detail the approach you have taken
- Visualise the output of the model using an appropriate format
- Explain model selection and justification as to why this was chosen over other approaches
- Success criteria
- Highlight any current limitations in the model
- Discuss subsequent next steps
We will not be looking into the details of the paper.
[1] Dispenzieri, A., Katzmann, J., Kyle, R., Larson, D., Therneau, T., Colby, C., Clark, R., Mead, G., Kumar, S., Melton III, LJ. and Rajkumar, SV. Use of monclonal serum immunoglobulin free light chains to predict overall survival in the general population, Mayo Clinic Proceedings 87:512-523. (2012)