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The Role of Artificial Intelligence in Predicting & Detecting Heart Diseases Early

Date: 24-03-2023

Heart Diseases on The Rise

Heart disease is the leading cause of death worldwide, responsible for around 18 million deaths annually. Early detection and timely intervention can significantly reduce the risk of mortality and morbidity associated with heart disease. Artificial Intelligence (AI) has emerged as a promising tool to aid in the prediction and prevention of heart disease. In this blog, we will explore the role of AI in heart disease prediction.

Role Of Artificial Intelligence in Healthcare

AI has been applied to various areas of healthcare, including medical image analysis, disease diagnosis, and prediction. In the case of heart disease prediction, Artificial Intelligence algorithms are trained on large datasets of patient information to learn patterns and identify risk factors associated with heart disease. This information can be used to create predictive models that can assist clinicians in identifying patients who are at high risk of developing heart disease.

AI Techniques Used for Heart Disease Healthcare

One of the most common Artificial Intelligence techniques used for heart disease prediction is machine learning. Machine learning algorithms can be trained on large datasets of patient information, such as demographic data, medical history, laboratory results, and imaging data.

These algorithms can identify patterns and relationships between risk factors and the development of heart disease, which can then be used to predict a patient’s risk of developing heart disease.

AI For Heart Disease Prediction

Several studies have shown the effectiveness of AI in heart disease prediction. For example, a study published in the European Heart Journal in 2019 used a machine learning algorithm to predict the risk of heart disease in more than 378,000 patients. The algorithm was able to accurately predict the risk of heart disease in these patients, outperforming traditional risk assessment tools.

Another study published in the Journal of the American College of Cardiology in 2018 used deep learning, a subset of machine learning, to predict the presence of heart disease in patients. The deep learning algorithm was trained on more than 1.3 million echocardiogram images and was able to accurately predict the presence of heart disease in patients with high accuracy.

Early Detection of Heart Disease

AI can also assist in the early detection of heart disease. For example, a study published in the Journal of the American College of Cardiology in 2020 used machine learning to analyze electrocardiogram (ECG) data and identify patients that can be having high risks of developing a type of heart arrhythmia called atrial fibrillation. The algorithm was able to identify high-risk patients several months before the onset of symptoms, allowing for early intervention and treatment.

Ways Of Utilizing AI For Cardiac Diseases

Artificial intelligence (AI) has the potential to revolutionize the field of cardiac disease prediction and management. Here are some ways in which AI is being utilized for predicting cardiac diseases:

Risk prediction

AI algorithms can analyze large amounts of patient data, such as medical history, lifestyle, and test results, to accurately predict a patient’s risk of developing heart disease. This can help clinicians develop personalized prevention strategies.

Image analysis

AI can analyze images of the heart, such as echocardiograms, to detect abnormalities that may indicate cardiac disease. This can help clinicians make earlier and more accurate diagnoses.

Patient monitoring

Wearable devices, such as smartwatches, can collect continuous data on a patient’s heart rate, blood pressure, and other vital signs. AI algorithms can analyze this data to detect patterns or anomalies that may indicate a risk of cardiac disease or a need for intervention.

Treatment optimization

AI can analyze patient data to determine the most effective treatment options for cardiac disease. This can help clinicians develop personalized treatment plans that are tailored to each patient’s unique needs.

Overall, AI has the potential to improve the accuracy and efficiency of cardiac disease prediction and management, leading to better patient outcomes and reduced healthcare costs.

Code Example

Python code for a simple Logistic Regression Analysis to understand the basic procedures of Machine learning modeling in medical science:

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    “colab”: {
      “provenance”: []
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    “kernelspec”: {
      “name”: “python3”,
      “display_name”: “Python 3”
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    “language_info”: {
      “name”: “python”
    }
  },
  “cells”: [
    {
      “cell_type”: “markdown”,
      “source”: [
        “#Some comman but required libraries”
      ],
      “metadata”: {
        “id”: “KVc_Pr9a1Prz”
      }
    },
    {
      “cell_type”: “code”,
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      “metadata”: {
        “id”: “KmWR6aGUQzDn”
      },
      “outputs”: [],
      “source”: [
        “import numpy as np\n”,
        “import pandas as pd\n”,
        “import seaborn as sns\n”,
        “import matplotlib.pyplot as plt\n”,
        “from sklearn.model_selection import train_test_split\n”,
        “from sklearn.linear_model import LogisticRegression\n”,
        “from sklearn.metrics import confusion_matrix, classification_report”
      ]
    },
    {
      “cell_type”: “markdown”,
      “source”: [
        “# # Load the dataset from UCI machine-learning library named processed cleaveland data”
      ],
      “metadata”: {
        “id”: “RUurbwEu1iSl”
      }
    },
    {
      “cell_type”: “code”,
      “source”: [
        “data = pd.read_csv(‘https://archive.ics.uci.edu/ml/machine-learning-databases/heart-disease/processed.cleveland.data’, header=None)”
      ],
      “metadata”: {
        “id”: “WaAlXlgvQ0qq”
      },
      “execution_count”: 2,
      “outputs”: []
    },
    {
      “cell_type”: “markdown”,
      “source”: [
        “# Make the column names as following”
      ],
      “metadata”: {
        “id”: “eQjtx4Yl3fGM”
      }
    },
    {
      “cell_type”: “code”,
      “source”: [
        “col_names = [‘age’, ‘sex’, ‘cp’, ‘trestbps’, ‘chol’, ‘fbs’, ‘restecg’, ‘thalach’, ‘exang’, ‘oldpeak’, ‘slope’, ‘ca’, ‘thal’, ‘target’]\n”,
        “data.columns = col_names”
      ],
      “metadata”: {
        “id”: “ITEXaLdDRDCc”
      },
      “execution_count”: 3,
      “outputs”: []
    },
    {
      “cell_type”: “markdown”,
      “source”: [
        “# Do some pre processing like remove nan, ‘?’ and drop NA values”
      ],
      “metadata”: {
        “id”: “IgY5PAEC3vGV”
      }
    },
    {
      “cell_type”: “code”,
      “source”: [
        “data = data.replace(‘?’, np.nan)”
      ],
      “metadata”: {
        “id”: “f-WBpu9MRQ1H”
      },
      “execution_count”: 4,
      “outputs”: []
    },
    {
      “cell_type”: “code”,
      “source”: [
        “data = data.dropna()”
      ],
      “metadata”: {
        “id”: “pbO10L3ERWZT”
      },
      “execution_count”: 5,
      “outputs”: []
    },
    {
      “cell_type”: “code”,
      “source”: [
        “data.shape”
      ],
      “metadata”: {
        “colab”: {
          “base_uri”: “https://localhost:8080/”
        },
        “id”: “MGWrHTO65iPL”,
        “outputId”: “016afeb8-a35e-494d-98db-336a8440051f”
      },
      “execution_count”: 6,
      “outputs”: [
        {
          “output_type”: “execute_result”,
          “data”: {
            “text/plain”: [
              “(297, 14)”
            ]
          },
          “metadata”: {},
          “execution_count”: 6
        }
      ]
    },
    {
      “cell_type”: “markdown”,
      “source”: [
        “# For convinence collect all the target variables only as 0 and 1. 0 means not diseased and 1 means dignosed as deseased. “
      ],
      “metadata”: {
        “id”: “doRIhW4b4cry”
      }
    },
    {
      “cell_type”: “code”,
      “source”: [
        “data[‘target’] = np.where(data[‘target’] > 0, 1, 0)”
      ],
      “metadata”: {
        “id”: “AzNd1R_sRchC”
      },
      “execution_count”: 7,
      “outputs”: []
    },
    {
      “cell_type”: “markdown”,
      “source”: [
        “# Split the data in to train and testing data sets”
      ],
      “metadata”: {
        “id”: “T-oWJVXZ4spL”
      }
    },
    {
      “cell_type”: “code”,
      “source”: [
        “X = data.drop(‘target’, axis=1)\n”,
        “y = data[‘target’]”
      ],
      “metadata”: {
        “id”: “dHouOCAZRhHE”
      },
      “execution_count”: 8,
      “outputs”: []
    },
    {
      “cell_type”: “code”,
      “source”: [
        “X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)”
      ],
      “metadata”: {
        “id”: “vYy9KyUERiD7”
      },
      “execution_count”: 9,
      “outputs”: []
    },
    {
      “cell_type”: “markdown”,
      “source”: [
        “# Fit the Logistic regression ML model because our data set is having the binary type target variable.”
      ],
      “metadata”: {
        “id”: “x-hmrmdI5D7a”
      }
    },
    {
      “cell_type”: “code”,
      “source”: [
        “model = LogisticRegression()”
      ],
      “metadata”: {
        “id”: “105xhruhRw_B”
      },
      “execution_count”: 10,
      “outputs”: []
    },
    {
      “cell_type”: “code”,
      “source”: [
        “model.fit(X_train, y_train)”
      ],
      “metadata”: {
        “colab”: {
          “base_uri”: “https://localhost:8080/”,
          “height”: 213
        },
        “id”: “ViiPXScORz93”,
        “outputId”: “b65997d3-6d75-46f0-ad7a-cb71b4add26f”
      },
      “execution_count”: 11,
      “outputs”: [
        {
          “output_type”: “stream”,
          “name”: “stderr”,
          “text”: [
            “/usr/local/lib/python3.9/dist-packages/sklearn/linear_model/_logistic.py:458: ConvergenceWarning: lbfgs failed to converge (status=1):\n”,
            “STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n”,
            “\n”,
            “Increase the number of iterations (max_iter) or scale the data as shown in:\n”,
            ”    https://scikit-learn.org/stable/modules/preprocessing.html\n”,
            “Please also refer to the documentation for alternative solver options:\n”,
            ”    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n”,
            ”  n_iter_i = _check_optimize_result(\n”
          ]
        },
        {
          “output_type”: “execute_result”,
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            “text/plain”: [
              “LogisticRegression()”
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          },
          “metadata”: {},
          “execution_count”: 11
        }
      ]
    },
    {
      “cell_type”: “code”,
      “source”: [
        “y_pred = model.predict(X_test)”
      ],
      “metadata”: {
        “id”: “6E4NAeqQR2Tz”
      },
      “execution_count”: 12,
      “outputs”: []
    },
    {
      “cell_type”: “markdown”,
      “source”: [
        “# Check the accuracy of the trained model.”
      ],
      “metadata”: {
        “id”: “5m4hHOMd5Ipe”
      }
    },
    {
      “cell_type”: “code”,
      “source”: [
        “print(confusion_matrix(y_test, y_pred))\n”,
        “print(classification_report(y_test, y_pred))”
      ],
      “metadata”: {
        “colab”: {
          “base_uri”: “https://localhost:8080/”
        },
        “id”: “5Md5q6SDR9rV”,
        “outputId”: “fbf44988-6dc4-4469-fe89-760218ada53e”
      },
      “execution_count”: 13,
      “outputs”: [
        {
          “output_type”: “stream”,
          “name”: “stdout”,
          “text”: [
            “[[31  5]\n”,
            ” [ 3 21]]\n”,
            ”              precision    recall  f1-score   support\n”,
            “\n”,
            ”           0       0.91      0.86      0.89        36\n”,
            ”           1       0.81      0.88      0.84        24\n”,
            “\n”,
            ”    accuracy                           0.87        60\n”,
            ”   macro avg       0.86      0.87      0.86        60\n”,
            “weighted avg       0.87      0.87      0.87        60\n”,
            “\n”
          ]
        }
      ]
    }
  ]
}

In this code, we first import necessary libraries such as Pandas, Scikit-learn’s train_test_split for splitting data into training and testing sets, logistic regression from Scikit-learn’s linear model, and accuracy_score for evaluating model accuracy.

Then we load the heart disease dataset into a Pandas DataFrame and split it into training and testing sets with an 80-20 split using the train_test_split method.

Next, we create a logistic regression object and fit it to the training data using the fit() method.

We then use the predict() method to make predictions on the testing set and evaluate the model accuracy using accuracy_score().

Conclusion

In conclusion, Artificial Intelligence has emerged as a powerful tool in heart disease prediction and prevention. Machine learning algorithms can analyze large datasets of patient information to identify risk factors and predict a patient’s risk of developing heart disease. Deep learning algorithms can analyze medical images to detect the presence of heart disease with high accuracy.

Artificial Intelligence can also assist in the early detection of heart disease, allowing for timely intervention and treatment. As AI technology continues to advance, it is likely that its role in heart disease prediction and prevention will become even more significant in the future.

Tags associated Artificial Intelligence,Detecting Heart Diseases Early,Python Application,Python Application Development