Blockchain-ML medical health .ipynb

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      "/Users/tahirabbas/anaconda3/lib/python3.11/site-packages/pandas/core/arrays/masked.py:60: UserWarning: Pandas requires version '1.3.6' or newer of 'bottleneck' (version '1.3.5' currently installed).\n",
      "  from pandas.core import (\n"
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       "'/Users/tahirabbas'"
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   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import os\n",
    "from sklearn.model_selection import train_test_split\n",
    "from xgboost import XGBClassifier\n",
    "from sklearn.ensemble import RandomForestRegressor, VotingRegressor\n",
    "import seaborn as sns\n",
    "import xgboost as xgb\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from catboost import CatBoostClassifier\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "from catboost import CatBoostRegressor\n",
    "from xgboost import XGBRegressor\n",
    "from sklearn.ensemble import VotingRegressor\n",
    "import pandas as pd\n",
    "from catboost import CatBoostClassifier\n",
    "from xgboost import XGBRegressor\n",
    "from sklearn.ensemble import VotingRegressor\n",
    "import pandas as pd\n",
    "from sklearn.preprocessing import LabelEncoder, StandardScaler\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.ensemble import RandomForestClassifier, VotingClassifier\n",
    "from xgboost import XGBClassifier\n",
    "os.getcwd()"
   ]
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    "os.chdir('/Users/tahirabbas/Desktop/imp')"
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     "text": [
      "Enter Age: 19\n",
      "Enter Sex (Male/Female): 1\n",
      "Enter Cholesterol level: 1588\n",
      "Enter Blood Pressure: 120\n",
      "Enter Heart Rate: 98\n",
      "Enter Exercise Hours Per Week: 3\n",
      "Enter Physical Activity Days Per Week: 4\n",
      "Enter Sleep Hours Per Day: 8\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/tahirabbas/anaconda3/lib/python3.11/site-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but StandardScaler was fitted with feature names\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Enter a key to save the report: tahir0110\n",
      "Medical report created and saved successfully!\n"
     ]
    }
   ],
   "source": [
    "import time  \n",
    "\n",
    "class Blockchain:\n",
    "    def __init__(self):\n",
    "        self.chain = []\n",
    "        self.current_reports = []\n",
    "        self.create_block(proof=1, previous_hash='0')\n",
    "        self.reports = {}\n",
    "\n",
    "    def create_block(self, proof, previous_hash):\n",
    "        block = {\n",
    "            'index': len(self.chain) + 1,\n",
    "            'timestamp': time.time(),  \n",
    "            'proof': proof,\n",
    "            'previous_hash': previous_hash,\n",
    "            'reports': self.current_reports\n",
    "        }\n",
    "        self.chain.append(block)\n",
    "        self.current_reports = []\n",
    "        return block\n",
    "\n",
    "\n",
    "    def add_report(self, stress_value, diagnosis, key):\n",
    "        self.reports.setdefault(key, []).append({\n",
    "            'stress_value': stress_value,\n",
    "            'diagnosis': diagnosis\n",
    "        })\n",
    "        self.current_reports.append({\n",
    "            'stress_value': stress_value,\n",
    "            'diagnosis': diagnosis\n",
    "        })\n",
    "\n",
    "    def get_last_block(self):\n",
    "        return self.chain[-1]\n",
    "\n",
    "    def get_reports_by_key(self, key):\n",
    "        return self.reports.get(key, [])\n",
    "\n",
    "def load_and_prepare_data():\n",
    "    df = pd.read_csv('heart.csv')\n",
    "    label_encoder = LabelEncoder()\n",
    "    df['Sex'] = label_encoder.fit_transform(df['Sex'])\n",
    "    df['Blood Pressure'] = df['Blood Pressure'].str.replace('/', '').astype(float)\n",
    "    df.drop(['Patient ID', 'Diabetes', 'Family History', \"Smoking\", 'Alcohol Consumption', 'Diet', 'Previous Heart Problems', 'Medication Use', 'Sedentary Hours Per Day', 'Income', 'BMI', 'Triglycerides', 'Country', 'Continent', 'Hemisphere', 'Heart Attack Risk', 'Obesity'], axis=1, inplace=True)\n",
    "    y = df['Stress Level']\n",
    "    X = df.drop([\"Stress Level\"], axis=1)\n",
    "    return X, y\n",
    "\n",
    "def train_model(X, y):\n",
    "    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
    "    scaler = StandardScaler()\n",
    "    X_train_scaled = scaler.fit_transform(X_train)\n",
    "    X_test_scaled = scaler.transform(X_test)\n",
    "    rf_clf = RandomForestClassifier(random_state=42)\n",
    "    xgb_clf = XGBClassifier(random_state=42)\n",
    "    voting_clf = VotingClassifier(estimators=[('rf', rf_clf), ('xgb', xgb_clf)], voting='soft')\n",
    "    voting_clf.fit(X_train_scaled, y_train)\n",
    "    return voting_clf, scaler\n",
    "\n",
    "def predict_stress_level(model, scaler, user_input):\n",
    "    user_input_scaled = scaler.transform([user_input])\n",
    "    predicted_stress_level = model.predict(user_input_scaled)[0]\n",
    "    return predicted_stress_level\n",
    "\n",
    "def get_diagnosis(stress_level):\n",
    "    if stress_level <= 3:\n",
    "        return \"Stress level is in control.\"\n",
    "    elif stress_level <= 7:\n",
    "        return \"Moderate stress level. Do some outdoor activity.\"\n",
    "    else:\n",
    "        return \"Consult a doctor, your stress level is very high.\"\n",
    "\n",
    "def get_user_input():\n",
    "    age = float(input(\"Enter Age: \"))\n",
    "    sex = input(\"Enter Sex (Male/Female): \")\n",
    "    sex = 1 if sex.lower() == 'male' else 0\n",
    "    cholesterol = float(input(\"Enter Cholesterol level: \"))\n",
    "    blood_pressure = float(input(\"Enter Blood Pressure: \"))\n",
    "    heart_rate = float(input(\"Enter Heart Rate: \"))\n",
    "    exercise_hours_per_week = float(input(\"Enter Exercise Hours Per Week: \"))\n",
    "    physical_activity_days_per_week = float(input(\"Enter Physical Activity Days Per Week: \"))\n",
    "    sleep_hours_per_day = float(input(\"Enter Sleep Hours Per Day: \"))\n",
    "    \n",
    "    user_input = [age, sex, cholesterol, blood_pressure, heart_rate, exercise_hours_per_week, physical_activity_days_per_week, sleep_hours_per_day]\n",
    "    return user_input\n",
    "\n",
    "X, y = load_and_prepare_data()  \n",
    "model, scaler = train_model(X, y)\n",
    "user_input = get_user_input()\n",
    "predicted_stress_level = predict_stress_level(model, scaler, user_input)\n",
    "diagnosis = get_diagnosis(predicted_stress_level)\n",
    "\n",
    "blockchain = Blockchain()\n",
    "key = input(\"Enter a key to save the report: \")\n",
    "blockchain.add_report(predicted_stress_level, diagnosis, key)\n",
    "print(\"Medical report created and saved successfully!\")\n",
    "\n"
   ]
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    "Enter Age: 19\n",
    "Enter Sex (Male/Female): 1\n",
    "Enter Cholesterol level: 1588\n",
    "Enter Blood Pressure: 120\n",
    "Enter Heart Rate: 98\n",
    "Enter Exercise Hours Per Week: 3\n",
    "Enter Physical Activity Days Per Week: 4\n",
    "Enter Sleep Hours Per Day: 8"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "18fe4e7a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Enter your key to retrieve the reports: tahir0110\n",
      "All medical reports associated with the key:\n",
      "Report 1: Stress Value: 1, Diagnosis: Stress level is in control.\n"
     ]
    }
   ],
   "source": [
    "def retrieve_reports(blockchain, key):\n",
    "    reports = blockchain.reports.get(key, None)\n",
    "    if reports is None:\n",
    "        return \"No reports found for this key.\"\n",
    "    else:\n",
    "        return reports\n",
    "\n",
    "key_to_retrieve = input(\"Enter your key to retrieve the reports: \")\n",
    "reports = retrieve_reports(blockchain, key_to_retrieve)\n",
    "\n",
    "if isinstance(reports, list):\n",
    "    print(\"All medical reports associated with the key:\")\n",
    "    for i, report in enumerate(reports, start=1):\n",
    "        print(f\"Report {i}: Stress Value: {report['stress_value']}, Diagnosis: {report['diagnosis']}\")\n",
    "else:\n",
    "    print(reports)"
   ]
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   "source": [
    "df = pd.read_csv('heart.csv')\n",
    "df.shape"
   ]
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