In this project, I applied machine learning techniques to classify blood analysis results into different categories. The main objective was to develop a model that can accurately predict the presence of certain conditions based on the blood analysis values.
The first step in this project was to collect the blood analysis data from various sources. The data consisted of several features such as red blood cell count, white blood cell count, hemoglobin level, platelet count, etc. I then preprocessed the data by removing any missing values and normalizing the features to ensure that they were on the same scale.
Next, I performed feature selection to identify the most important features that would be used in the classification model. I used various techniques such as correlation analysis and feature importance ranking to select the top features.
I then developed several machine learning models, including logistic regression, decision tree, and random forest. I trained the models on the preprocessed data and evaluated their performance using various metrics such as accuracy, precision, recall, and F1 score. I also performed cross-validation to ensure that the models were not overfitting the data.
The final model was a random forest classifier that achieved an accuracy of 90% on the test data. The most important features for the classification were red blood cell count, white blood cell count, and platelet count.
In conclusion, this project demonstrated the effectiveness of machine learning techniques in classifying blood analysis results. The developed model can be used to aid in the diagnosis of various blood-related conditions and improve patient outcomes.