Applying Machine Learning Models to Predict The Presence of Heart Disease in Humans

Applying Machine Learning Models to Predict The Presence of Heart Disease in Humans

I’m going to explore two Machine Learning models, Logistic Regression and K Nearest Neighbor, and implement them to predict diagnosis for the presence of Heart Disease in Humans.

Artificial Intelligence(A.I) is gradually taking over many industries and automating tasks with high efficiency and accuracy. Machine Learning is an application of A.I, where systems learn from data and improve without explicit programming. One of the applications of Machine Learning in the medical field is predicting diagnosis for different diseases and conditions.

It’s important to have a model with high accuracy since the predictions are concerned with the health of human beings, so it is necessary to test different models and see which one provides a better result.

I’m going to explore two Machine Learning models, Logistic Regression and K Nearest Neighbor, and implement them to predict diagnosis for the presence of Heart Disease in Humans. The dataset I’m gonna be using comes from UCI and can be found on kaggle(link on the bottom of this article).

I’m gonna use Jupyter Notebook as my environment, Python3 as the programming language, Seaborn and matplotlib for data visualization, and SKLearn library for Machine Learning models and metrics.

Let’s import the libraries first:

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn.linear_model import LogisticRegression

k-nearest-neighbours ai data-science logistic-regression machine-learning

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