Machine Learning Algorithms to Detect Heart Diseases

Background: The heart is one of our body's most essential organs, pumping blood to various organs via veins, arteries. Heart disease is a disorder that impairs the heart's ability to operate. It has now evolved into a serious illness that shortens human life.

Objectives: A difficult challenge in the healthcare industry is forecasting cardiac disease data which refers to a large volume of data collected at a rapid pace. These algorithmic approaches [1] will be implemented, executed on the dataset to give best results so that medical practitioners can make rapid 'decisions and diagnose accurately.

Methodology: Algorithms Used: The algorithms used are Perceptron, Decision Tree Classifier [2], Random Forest, and K-Neighbors [3].

Libraries used: Numpy: It is the most important Python module for scientific computing and mathematical computations. Scikit-learn: -Sk learn in Python, Scikit-learn (Sklearn) is the most usable and robust machine learning package. Pandas: It is a data manipulation and analysis software package for the Python programming language. Matplotlib: It is used for plotting graphs from numeric data in python.

Results and Discussion: If two supervised learning algorithms were used in separate experiments, there's a risk that a performance comparison between them might provide error results. These studies employed a variety of factors or measurements to predict illness [4].

Conclusion and future scope: Due to the scarcity of symptom and diagnostic data, data processing and validation of heart-related diseases is a difficult task. The future scope involves using a hybrid ML algorithm utilizing Decision Tree's features with X-ray image classification system for better prediction of results [5].