Machine Learning for Medical Conditions SMOTE-Based Data Balancing Approach

This project will require a medical conditions dataset to build a predicting model that can classify the level of health risk and medical conditions based on various clinical and demographic features. With this, the major objective is always the identification of different levels of health risk based on other metrics like BMI and blood pressure or glucose. Other types of demographic factors considered are gender and smoking status. All the missing values in the columns of numerical variables were imputed by median values so as to prevent any skew in the results even though part of the data were not complete; categorical features that include gender and smoking status were changed into dummy variables. This helps in ensuring such features are in a machine-friendly format. Numerical features are standardized to bring the scale of the feature to a standard, procedure that typically improves the model's performance by not allowing features with larger ranges from dominating the model. Therefore, through an analysis of these features, this project should help identify risk factors that can contribute to poor health outcomes and subsequently come up with a framework that predicts This kind of predictive model crucial in healthcare, A set of Exploratory Data Analyses (EDA) were performed in order to understand the relations between the features and the overall nature of the data through series of experiments. Distributions of the features are visualized by histograms, for any categorical variables bar plots represent, and numerical features draw potential interdependencies via their correlation heatmaps. Similarly, for some features pair-plots are generated while in case of exploring any type of a linear or non-linear kind ofrelationship that would highly affect the prediction generated through the model. Finally, the random forest classifier model was fitted to predict specific health conditions based on the feature set extracted. The dataset was split for training and testing purposes (70% 30%). Model performance for precision, recall, and F1-score were noted. Such an analysis presents the strength of the predictive model concerning accuracy and its feasibility in clinical screening applications. In summary, the project will provide a systematic approach toward predicting health risks and conditions through careful data preprocessing, exploratory analysis, and a robust classification model.