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page_name = F.__name__ + print(f"Creating {page_name} frame.") + frame = F(parent=self.container, controller=self) + self.frames[page_name] = frame + frame.grid(row=0, column=0, sticky="nsew") + print(f"{page_name} frame created and added to frames dictionary.") # Debug + + print("Showing Homepage frame.") + self.show_frame("Homepage") + self.protocol("WM_DELETE_WINDOW", self.on_closing) + + def show_frame(self, page_name): + print(f"Attempting to show {page_name} frame.") # Debug + frame = self.frames.get(page_name) + if frame: + print(f"Raising {page_name} frame.") # Debug + frame.tkraise() + if page_name == "Homepage": + print("Calling show_homepage on Homepage frame.") # Debug + frame.show_homepage() + else: + print(f"Frame {page_name} not found in frames dictionary.") # Debug + + def on_closing(self): + """Delete data.csv when the application is closed.""" + if os.path.exists("data.csv"): + os.remove("data.csv") + self.destroy() + +if __name__ == "__main__": + app = MainApplication() + app.mainloop() diff --git a/README.md b/README.md new file mode 100644 index 0000000000000000000000000000000000000000..d528ce3a44e5a58ccfdd89ef4dfa34ab47d31287 --- /dev/null +++ b/README.md @@ -0,0 +1,93 @@ +# SDGP_FeedingDashboard + + + +## Getting started + +To make it easy for you to get started with GitLab, here's a list of recommended next steps. + +Already a pro? Just edit this README.md and make it your own. Want to make it easy? [Use the template at the bottom](#editing-this-readme)! + +## Add your files + +- [ ] [Create](https://docs.gitlab.com/ee/user/project/repository/web_editor.html#create-a-file) or [upload](https://docs.gitlab.com/ee/user/project/repository/web_editor.html#upload-a-file) files +- [ ] [Add files using the command line](https://docs.gitlab.com/topics/git/add_files/#add-files-to-a-git-repository) or push an existing Git repository with the following command: + +``` +cd existing_repo +git remote add origin https://gitlab.uwe.ac.uk/h2-veziroglu/sdgp_feedingdashboard.git +git branch -M main +git push -uf origin main +``` + +## Integrate with your tools + +- [ ] [Set up project integrations](https://gitlab.uwe.ac.uk/h2-veziroglu/sdgp_feedingdashboard/-/settings/integrations) + +## Collaborate with your team + +- [ ] [Invite team members and collaborators](https://docs.gitlab.com/ee/user/project/members/) +- [ ] [Create a new merge request](https://docs.gitlab.com/ee/user/project/merge_requests/creating_merge_requests.html) +- [ ] [Automatically close issues from merge requests](https://docs.gitlab.com/ee/user/project/issues/managing_issues.html#closing-issues-automatically) +- [ ] [Enable merge request approvals](https://docs.gitlab.com/ee/user/project/merge_requests/approvals/) +- [ ] [Set auto-merge](https://docs.gitlab.com/user/project/merge_requests/auto_merge/) + +## Test and Deploy + +Use the built-in continuous integration in GitLab. + +- [ ] [Get started with GitLab CI/CD](https://docs.gitlab.com/ee/ci/quick_start/) +- [ ] [Analyze your code for known vulnerabilities with Static Application Security Testing (SAST)](https://docs.gitlab.com/ee/user/application_security/sast/) +- [ ] [Deploy to Kubernetes, Amazon EC2, or Amazon ECS using Auto Deploy](https://docs.gitlab.com/ee/topics/autodevops/requirements.html) +- [ ] [Use pull-based deployments for improved Kubernetes management](https://docs.gitlab.com/ee/user/clusters/agent/) +- [ ] [Set up protected environments](https://docs.gitlab.com/ee/ci/environments/protected_environments.html) + +*** + +# Editing this README + +When you're ready to make this README your own, just edit this file and use the handy template below (or feel free to structure it however you want - this is just a starting point!). Thanks to [makeareadme.com](https://www.makeareadme.com/) for this template. + +## Suggestions for a good README + +Every project is different, so consider which of these sections apply to yours. The sections used in the template are suggestions for most open source projects. Also keep in mind that while a README can be too long and detailed, too long is better than too short. If you think your README is too long, consider utilizing another form of documentation rather than cutting out information. + +## Name +Choose a self-explaining name for your project. + +## Description +Let people know what your project can do specifically. Provide context and add a link to any reference visitors might be unfamiliar with. A list of Features or a Background subsection can also be added here. If there are alternatives to your project, this is a good place to list differentiating factors. + +## Badges +On some READMEs, you may see small images that convey metadata, such as whether or not all the tests are passing for the project. You can use Shields to add some to your README. Many services also have instructions for adding a badge. + +## Visuals +Depending on what you are making, it can be a good idea to include screenshots or even a video (you'll frequently see GIFs rather than actual videos). Tools like ttygif can help, but check out Asciinema for a more sophisticated method. + +## Installation +Within a particular ecosystem, there may be a common way of installing things, such as using Yarn, NuGet, or Homebrew. However, consider the possibility that whoever is reading your README is a novice and would like more guidance. Listing specific steps helps remove ambiguity and gets people to using your project as quickly as possible. If it only runs in a specific context like a particular programming language version or operating system or has dependencies that have to be installed manually, also add a Requirements subsection. + +## Usage +Use examples liberally, and show the expected output if you can. It's helpful to have inline the smallest example of usage that you can demonstrate, while providing links to more sophisticated examples if they are too long to reasonably include in the README. + +## Support +Tell people where they can go to for help. It can be any combination of an issue tracker, a chat room, an email address, etc. + +## Roadmap +If you have ideas for releases in the future, it is a good idea to list them in the README. + +## Contributing +State if you are open to contributions and what your requirements are for accepting them. + +For people who want to make changes to your project, it's helpful to have some documentation on how to get started. Perhaps there is a script that they should run or some environment variables that they need to set. Make these steps explicit. These instructions could also be useful to your future self. + +You can also document commands to lint the code or run tests. These steps help to ensure high code quality and reduce the likelihood that the changes inadvertently break something. Having instructions for running tests is especially helpful if it requires external setup, such as starting a Selenium server for testing in a browser. + +## Authors and acknowledgment +Show your appreciation to those who have contributed to the project. + +## License +For open source projects, say how it is licensed. + +## Project status +If you have run out of energy or time for your project, put a note at the top of the README saying that development has slowed down or stopped completely. Someone may choose to fork your project or volunteer to step in as a maintainer or owner, allowing your project to keep going. You can also make an explicit request for maintainers. diff --git a/__pycache__/app1.cpython-311.pyc b/__pycache__/app1.cpython-311.pyc new file mode 100644 index 0000000000000000000000000000000000000000..80fd58da2a0d081754bdb281d955bb296ed5dbf1 Binary files /dev/null and b/__pycache__/app1.cpython-311.pyc differ diff --git a/__pycache__/app1.cpython-312.pyc b/__pycache__/app1.cpython-312.pyc new file mode 100644 index 0000000000000000000000000000000000000000..20dad1a7731d1584bb675ecf776ecf610e35ce4a Binary files /dev/null and b/__pycache__/app1.cpython-312.pyc differ diff --git a/__pycache__/feeding_dashboard.cpython-311.pyc b/__pycache__/feeding_dashboard.cpython-311.pyc new file mode 100644 index 0000000000000000000000000000000000000000..f5ec618d1b235e3415827b4a5a9e44e114a00bd4 Binary files /dev/null and b/__pycache__/feeding_dashboard.cpython-311.pyc differ diff --git a/__pycache__/feeding_dashboard.cpython-312.pyc b/__pycache__/feeding_dashboard.cpython-312.pyc new file mode 100644 index 0000000000000000000000000000000000000000..d0a5dbed9c9eddd662a1d7d4d7023b850e89643e Binary files /dev/null and b/__pycache__/feeding_dashboard.cpython-312.pyc differ diff --git a/app1.py b/app1.py new file mode 100644 index 0000000000000000000000000000000000000000..219f74a27d46e74fdeb7b56b38c6c841d4b409ae --- /dev/null +++ b/app1.py @@ -0,0 +1,92 @@ +import tkinter as tk +from tkinter import filedialog, ttk +import pandas as pd +import matplotlib.pyplot as plt +from matplotlib.backends.backend_tkagg import FigureCanvasTkAgg + +class Homepage(tk.Frame): + def __init__(self, parent, controller): + super().__init__(parent) + self.controller = controller + + self.sidebar = tk.Frame(self, bg='#2c3e50', width=220, height=800) + self.sidebar.pack(side=tk.LEFT, fill=tk.Y) + + self.home_btn = tk.Button(self.sidebar, text="Homepage", command=lambda: controller.show_frame("Homepage"), bg='#3498db', font=("Arial", 12)) + self.home_btn.pack(pady=10, padx=10, fill=tk.X) + + self.upload_btn = tk.Button(self.sidebar, text="Upload CSV", command=lambda: controller.show_frame("UploadPage"), bg='#27ae60', font=("Arial", 12)) + self.upload_btn.pack(pady=10, padx=10, fill=tk.X) + + self.main_frame = tk.Frame(self, bg='white', width=980, height=800) + self.main_frame.pack(fill="both", expand=True) + + self.data = None + self.show_homepage() + + def show_homepage(self): + for widget in self.main_frame.winfo_children(): + widget.destroy() + + tk.Label(self.main_frame, text="Patient Referral Dashboard", font=("Arial", 20), bg='white').pack(pady=10) + + try: + self.data = pd.read_csv("data.csv") + self.generate_charts() + except (FileNotFoundError, pd.errors.EmptyDataError): + tk.Label(self.main_frame, text="No data available. Please upload data in the Upload Data page.", + font=("Arial", 14), bg='white').pack(pady=20) + + + def generate_charts(self): + """Generate and display charts only if data is available.""" + if self.data is None or self.data.empty: + return + + total_patients = len(self.data) + referral_patients = self.data['referral'].sum() + non_referral_patients = total_patients - referral_patients + + fig, axes = plt.subplots(1, 2, figsize=(14, 6)) + + labels = ['Need Referral', 'Do Not Need Referral'] + sizes = [referral_patients, non_referral_patients] + axes[0].pie(sizes, labels=labels, autopct='%1.1f%%', startangle=100, colors=['#66c2a5', '#fc8d62']) + axes[0].set_title("Patient Referral Distribution", fontsize=16) + + average_data = self.data.drop(columns=['encounterId', 'referral']).mean() + axes[1].bar(average_data.index, average_data.values, color='#1f77b4', width=0.4) + axes[1].set_xticklabels(average_data.index, rotation=45, ha='right', fontsize=10) + axes[1].set_title("Average Patient Data", fontsize=14) + + plt.tight_layout() + + canvas = FigureCanvasTkAgg(fig, master=self.main_frame) + canvas.draw() + canvas.get_tk_widget().pack(pady=10) + + def show_data(self): + """Display the uploaded CSV data in a table format.""" + for widget in self.main_frame.winfo_children(): + widget.destroy() + + if self.data is not None: + tree = ttk.Treeview(self.main_frame) + tree['columns'] = tuple(self.data.columns) + + for col in self.data.columns: + tree.heading(col, text=col) + tree.column(col, width=100) + + for index, row in self.data.iterrows(): + tree.insert("", "end", values=tuple(row)) + + tree.pack(fill=tk.BOTH, expand=True) + else: + tk.Label(self.main_frame, text="No Data Available", font=("Arial", 14), bg='white').pack(pady=20) + + def on_closing(self): + """Delete data.csv when the dashboard is closed.""" + if os.path.exists("data.csv"): + os.remove("data.csv") + self.root.destroy() diff --git a/dashboard_data1.csv b/dashboard_data1.csv new file mode 100644 index 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+5997,0.0,0.10302742115664457,0.18867924528301888,0.0,0.0,0.0,0.07741935483870968,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.5866396760925744,0 diff --git a/dietitian_model.pkl b/dietitian_model.pkl new file mode 100644 index 0000000000000000000000000000000000000000..915c2de82f75509988a0c8af6235cdf9cba9b580 Binary files /dev/null and b/dietitian_model.pkl differ diff --git a/feeding_dashboard.py b/feeding_dashboard.py new file mode 100644 index 0000000000000000000000000000000000000000..76effee118871d222033a9469c6c44ec81529762 --- /dev/null +++ b/feeding_dashboard.py @@ -0,0 +1,175 @@ +import tkinter as tk +from tkinter import filedialog, messagebox, ttk +import pandas as pd +import numpy as np +import joblib +import subprocess + +model = joblib.load("dietitian_model.pkl") + +REQUIRED_COLUMNS = ["end_tidal_co2", "feed_vol", "feed_vol_adm", "fio2", "fio2_ratio", + "insp_time", "oxygen_flow_rate", "peep", "pip", "resp_rate", "sip", + "tidal_vol", "tidal_vol_actual", "tidal_vol_kg", "tidal_vol_spon", "bmi"] + +class UploadPage(tk.Frame): + def __init__(self, parent, controller): + super().__init__(parent) + self.controller = controller + + self.sidebar = tk.Frame(self, bg='#2c3e50', width=220, height=800) + self.sidebar.pack(side=tk.LEFT, fill=tk.Y) + + self.home_btn = tk.Button(self.sidebar, text="Homepage", command=lambda: self.controller.show_frame("Homepage"), bg='#3498db', font=("Arial", 12)) + self.home_btn.pack(pady=10, padx=10, fill=tk.X) + + self.upload_btn = tk.Button(self.sidebar, text="Upload CSV", command=self.upload_csv, bg='#27ae60', font=("Arial", 12)) + self.upload_btn.pack(pady=10, padx=10, fill=tk.X) + + self.main_frame = tk.Frame(self) + self.main_frame.pack(fill="both", expand=True) + + self.filter_frame = tk.Frame(self.main_frame) + self.filter_frame.pack(side="top", fill="x", padx=10, pady=5) + + self.search_var = tk.StringVar() + self.search_entry = tk.Entry(self.filter_frame, textvariable=self.search_var, width=20) + self.search_entry.pack(side="left", padx=5) + self.search_entry.bind("<KeyRelease>", self.search_patient) + + self.show_all_btn = tk.Button(self.filter_frame, text="Show All", command=self.show_all) + self.show_all_btn.pack(side="left", padx=5) + + self.need_referral_btn = tk.Button(self.filter_frame, text="Need Referral", command=lambda: self.filter_by_status("Need Referral")) + self.need_referral_btn.pack(side="left", padx=5) + + self.no_need_referral_btn = tk.Button(self.filter_frame, text="No Need Referral", command=lambda: self.filter_by_status("No Need Referral")) + self.no_need_referral_btn.pack(side="left", padx=5) + + self.table_frame = tk.Frame(self.main_frame) + self.table_frame.pack(side="left", fill="both", expand=True, padx=10, pady=10) + + self.tree = ttk.Treeview(self.table_frame, columns=("ID", "Referral"), show="headings") + self.tree.heading("ID", text="Patient ID") + self.tree.heading("Referral", text="Dietitian Referral") + self.tree.bind("<Double-1>", self.view_patient_details) + self.tree.pack(fill="both", expand=True) + + self.details_frame = tk.Frame(self.main_frame, relief=tk.SUNKEN, borderwidth=2) + self.details_visible = False + + tk.Button(self, text="Upload New CSV", command=self.upload_csv).pack(pady=5) + + self.patients = [] + + def open_homepage(self): + self.controller.show_frame("Homepage") + + def close_patient_details(self): + """Hide patient details panel.""" + if self.details_visible: + self.details_frame.pack_forget() + self.details_visible = False + + def view_patient_details(self, event): + """Always show details when a patient is clicked.""" + selected_item = self.tree.selection() + if not selected_item: + return + + patient_id = int(float(self.tree.item(selected_item, "values")[0])) + + patient = next((p for p in self.patients if p["encounterId"] == patient_id), None) + if patient: + for widget in self.details_frame.winfo_children(): + widget.destroy() + + self.details_label = tk.Label(self.details_frame, text="Patient Details", font=("Arial", 14, "bold")) + self.details_label.pack(anchor="n", pady=5) + + close_button = tk.Button(self.details_frame, text="✖", font=("Arial", 12, "bold"), + command=self.close_patient_details, relief=tk.FLAT, + bg="white", fg="gray", width=2, height=1) + close_button.pack(anchor="ne", padx=3, pady=3) + + self.details_table = ttk.Treeview(self.details_frame, columns=("Attribute", "Value"), show="headings") + self.details_table.heading("Attribute", text="Attribute") + self.details_table.heading("Value", text="Value") + self.details_table.pack(fill="both", expand=True, padx=5, pady=5) + + referral_status = "Need Referral" if patient['referral'] else "No Need Referral" + self.details_table.insert("", "end", values=("Patient ID", patient['encounterId'])) + self.details_table.insert("", "end", values=("Referral Status", referral_status), tags=(referral_status,)) + self.details_table.tag_configure("Need Referral", background="#FFDDDD") + self.details_table.tag_configure("No Need Referral", background="#DDEEFF") + + for key in REQUIRED_COLUMNS: + self.details_table.insert("", "end", values=(key.replace('_', ' ').title(), patient[key])) + + self.details_frame.pack(side="right", fill="both", expand=True, padx=10, pady=10) + self.details_visible = True + + def search_patient(self, event=None): + """Search by Patient ID dynamically.""" + search_query = self.search_var.get().strip() + self.tree.delete(*self.tree.get_children()) + for patient in self.patients: + if search_query.isdigit() and str(patient["encounterId"]).startswith(search_query): + referral_status = "Need Referral" if patient["referral"] else "No Need Referral" + self.tree.insert("", "end", values=(patient["encounterId"], referral_status), tags=(referral_status,)) + + def filter_by_status(self, status): + """Filter patients by referral status.""" + self.tree.delete(*self.tree.get_children()) + for patient in self.patients: + referral_status = "Need Referral" if patient["referral"] else "No Need Referral" + if referral_status == status: + self.tree.insert("", "end", values=(patient["encounterId"], referral_status), tags=(referral_status,)) + + def show_all(self): + """Show all patients.""" + self.tree.delete(*self.tree.get_children()) + for patient in self.patients: + referral_status = "Need Referral" if patient["referral"] else "No Need Referral" + self.tree.insert("", "end", values=(patient["encounterId"], referral_status), tags=(referral_status,)) + + def upload_csv(self): + """Upload CSV and update table.""" + file_path = filedialog.askopenfilename(filetypes=[("CSV Files", "*.csv")]) + if not file_path: + return + + df = pd.read_csv(file_path) + + if "encounterId" in df.columns: + df["encounterId"] = df["encounterId"].astype(int) + + trained_feature_names = list(model.feature_names_in_) + missing_cols = [col for col in trained_feature_names if col not in df.columns] + if missing_cols: + messagebox.showerror("Error", f"Missing columns: {', '.join(missing_cols)}") + return + + df = df[["encounterId"] + trained_feature_names] + self.patients.clear() + self.tree.delete(*self.tree.get_children()) + + feature_values = df[trained_feature_names].to_numpy() + referral_predictions = model.predict(feature_values) + + for i, row in df.iterrows(): + referral_prediction = int(referral_predictions[i]) + patient_entry = {"encounterId": int(row["encounterId"]), "referral": referral_prediction} + for key in trained_feature_names: + patient_entry[key] = row[key] + + self.patients.append(patient_entry) + referral_status = "Need Referral" if referral_prediction else "No Need Referral" + self.tree.insert("", "end", values=(int(row["encounterId"]), referral_status), tags=(referral_status,)) + + self.tree.tag_configure("Need Referral", background="#FFDDDD") + self.tree.tag_configure("No Need Referral", background="#DDEEFF") + + df["referral"] = referral_predictions + df.to_csv("data.csv", index=False) + + messagebox.showinfo("Success", "CSV uploaded successfully!") \ No newline at end of file diff --git a/ml_data1.csv b/ml_data1.csv new file mode 100644 index 0000000000000000000000000000000000000000..cf87a1d89686563db35e44dd8e46d6e31044d308 --- /dev/null +++ b/ml_data1.csv @@ -0,0 +1,1079 @@ +encounterId,end_tidal_co2,feed_vol,feed_vol_adm,fio2,fio2_ratio,insp_time,oxygen_flow_rate,peep,pip,resp_rate,sip,tidal_vol,tidal_vol_actual,tidal_vol_kg,tidal_vol_spon,bmi,referral +22020,0.0,0.06439213822290285,0.1179245283018868,0.0,0.0,0.0,0.033691756266666664,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.6630478955051171,0 +12624,0.0,0.5151371057832228,0.010444743933962265,0.0,0.0,0.0,0.033452807638709676,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.46205704413834486,0 +15458,0.1771830443543616,0.5151371057832228,0.03443396226415094,0.39893665159999997,0.17848171095218893,0.20987654314814813,0.0,0.4012731345331001,0.42204670333333333,0.0,0.0,0.6666666666666666,0.40255409778012685,0.384038351861644,0.0,0.47647058826608146,0 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"FeedingDashboardData.csv" +df = pd.read_csv(file_path) + + +REQUIRED_COLUMNS = [ + "end_tidal_co2", "feed_vol", "feed_vol_adm", "fio2", "fio2_ratio", + "insp_time", "oxygen_flow_rate", "peep", "pip", "resp_rate", "sip", + "tidal_vol", "tidal_vol_actual", "tidal_vol_kg", "tidal_vol_spon", "bmi", "referral" +] + + +missing_columns = [col for col in REQUIRED_COLUMNS if col not in df.columns] +if missing_columns: + print(f"⚠️ Missing columns in the uploaded data: {missing_columns}") + raise ValueError("Uploaded data is missing required columns.") + + +imputer = SimpleImputer(strategy="median") +df[REQUIRED_COLUMNS[:-1]] = imputer.fit_transform(df[REQUIRED_COLUMNS[:-1]]) + +# feetuee scaling +scaler = MinMaxScaler() +df[REQUIRED_COLUMNS[:-1]] = scaler.fit_transform(df[REQUIRED_COLUMNS[:-1]]) + + +train_data, ml_data = train_test_split(df, test_size=0.2, random_state=42) #Split DAta + + +train_data.to_csv("dashboard_data1.csv", index=False) +ml_data.to_csv("ml_data1.csv", index=False) + +print("✅ Data successfully validated, prepared, and saved!") diff --git a/test_model.py b/test_model.py new file mode 100644 index 0000000000000000000000000000000000000000..589d115451507bb661e274000d333e28d3c2dfc8 --- /dev/null +++ b/test_model.py @@ -0,0 +1,49 @@ +import unittest +import numpy as np +import joblib +import os + +class TestDietitianModel(unittest.TestCase): + def setUp(self): + # Load the model once for all tests + model_path = "dietitian_model.pkl" + self.assertTrue(os.path.exists(model_path), "Model file not found.") + self.model = joblib.load(model_path) + + # Example input (16 features in the correct order) + self.valid_input = np.array([[ + 35, # end_tidal_co2 + 250, # feed_vol + 245, # feed_vol_adm + 0.4, # fio2 + 300, # fio2_ratio + 1.2, # insp_time + 5, # oxygen_flow_rate + 6, # peep + 18, # pip + 22, # resp_rate + 5, # sip + 450, # tidal_vol + 460, # tidal_vol_actual + 7.5, # tidal_vol_kg + 100, # tidal_vol_spon + 23.5 # bmi + ]]) + + def test_model_predict_shape(self): + # Ensure the output has correct shape + prediction = self.model.predict(self.valid_input) + self.assertEqual(prediction.shape, (1,), "Prediction should return a single value.") + + def test_prediction_output_type(self): + # Ensure the prediction is a binary classification (0 or 1) + prediction = self.model.predict(self.valid_input)[0] + self.assertIn(prediction, [0, 1], "Prediction should be 0 or 1 (No Referral or Need Referral).") + + def test_model_input_size(self): + # Ensure the model expects 16 features + expected_features = 16 + self.assertEqual(self.valid_input.shape[1], expected_features, "Input should have 16 features.") + +if __name__ == "__main__": + unittest.main() diff --git a/train_model.py b/train_model.py new file mode 100644 index 0000000000000000000000000000000000000000..aab3b391ffdf2cd360942a741579cab1b408b05c --- /dev/null +++ b/train_model.py @@ -0,0 +1,36 @@ +import pandas as pd +import joblib +import numpy as np +from sklearn.model_selection import train_test_split +from sklearn.ensemble import RandomForestClassifier +from sklearn.metrics import classification_report, accuracy_score + + +df = pd.read_csv("ml_data1.csv") + +features = [ + "end_tidal_co2", "feed_vol", "feed_vol_adm", "fio2", "fio2_ratio", + "insp_time", "oxygen_flow_rate", "peep", "pip", "resp_rate", "sip", + "tidal_vol", "tidal_vol_actual", "tidal_vol_kg", "tidal_vol_spon", "bmi" +] + +df = df.dropna() + +X = df[features] +y = df["referral"] + +X.columns = features + +X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.8, random_state=42) + +model = RandomForestClassifier(n_estimators=100, random_state=42) +model.fit(X_train, y_train) + +joblib.dump(model, "dietitian_model.pkl") + +print("✅ Model trained and saved successfully using only 20% of the data!") + +y_pred = model.predict(X_test) +print("📈 Accuracy:", accuracy_score(y_test, y_pred)) +print("📊 Classification Report:\n", classification_report(y_test, y_pred)) +