Personalized Cancer Diagnosis

From the expert, the time to diagnosis of cancer takes a lot of time as it includes new studies/papers, which makes this a time-consuming and exhaustive process. With machine learning, we can fast-track the majority of scenarios and help the expert get updated details.

I have used below classical machine learning algorithms for the problem.

1: Naive Bayes

2: K Nearest Neighbors

3: Logistic Regression

4: Support Vector Machine (SVM)

5: Random Forest Classifier

6: StackedClassifier (Ensemble)

7: MaxVoting Classifier (Ensemble)

As you might know, these algorithms have their limitation and advantages, I have tried to incorporate the best use of them by remediating the problems. Like

• The curse of dimensionality has been addressed by Response Coding.
• Class imbalance can be tuned with stratified splits and using the Class weight parameter whenever exploitable.
• Compute intensive Hyper-Tuning with parallelism when needed.
• At last, the beautiful interface & ton of integration of streamlit used.