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HeartHealthPrediction

The major reason for the death in worldwide is the heart disease in high and low developed countries. The data scientist uses distinctive machine learning techniques for modeling health diseases by using authentic dataset eﬃciently and accurately. The medical analysts are needy for the models or systems to predict the disease in patients before the strike. High cholesterol, unhealthy diet, harmful use of alcohol, high sugar levels, high blood pressure, and smoking are the main symptoms of chances of the heart attack in humans. Data Science is an advanced and enhanced method for the analysis and encapsulation of useful information. The attributes and variable in the dataset discover an unknown and future state of the model using prediction in machine learning. Chest pain, blood pressure, cholesterol, blood sugar, family history of heart disease, obesity, and physical inactivity are the chances that inﬂuence the possibility of heart diseases. This project emphasizes to evaluate diﬀerent algorithms for the diagnosis of heart disease with better accuracies by using the patient’s data set because predictions and descriptions are fundamental objectives of machine learning. Each procedure has unique perspective for the modeling objectives. Algorithms have been implemented for the prediction of heart disease with our Heart patient data set

#data-science#decision-trees#healthcare#heart-health-prediction#meachinelearning#python-machine-learning

README preview

Dataset Information:

The dataset consists of 303 rows and 14 columns with label Target. Data contains categorical as well as continuous data.

Data Cleansing:

There is only one column that contains null value in which only two rows have null value that can easily be dropped by removing NA function.

Data Visualization:

Data Visualization is done by step by step process with critical analysis I use correlation matrix to find most dependent variable to the label which is Age. I plot graph of label (Target) to show the ratio of heart Disease.

Methodology:

To explain and identify the problem and resolve medical objectives, diﬀerent data Science technique, which interpret the medical goals, have been implemented to diagnose the heart disease and to improve the success standards of the algorithms for prediction. Suitable machine learning algorithms, like: Random Forest, SVM (Support Vector Machine), Decision Tree and Logistics Regression were preferred for the training and implementation in python for developing and evolving the predictive model. These algorithms executed on the model will help medical experts to predict and diagnose heart attacks in the patient dataset. The main goal is to identify which machine-learning algorithm has the best accuracy for the prediction of heart disease from the patient dataset.

Result:

Cross Validation is also done for all the models. The results are same but have some variance in accuracy. After Cross Validation the result become clear that Logistic regression is good for this problem

Installation Guide:

->Install Anaconda Destribution ->Install Jupyter Notebook ->Copy Heart Health.ipynb to path C:\Users\xyz along with heart.csv ->Run Jupyter Notebook and open file from its home page ->Change the path of read_csv() as your file location