Predicting Heart Failure Survival with Machine Learning Models 

Preface

Cardiovascular diseases are diseases of the heart and blood vessels and they typically include heart attacks, strokes, and heart failures [1]. According to the World Health Organization (WHO), cardiovascular diseases like ischaemic heart disease and stroke have been the leading causes of deaths worldwide for the last decade and a half [2].

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Motivation

A few months ago, a new heart failure dataset was uploaded on Kaggle. This dataset contained health records of 299 anonymized patients and had 12 clinical and lifestyle features. The task was to predict heart failure using these features.

Through this post, I aim to document my workflow on this task and present it as a research exercise. So this would naturally involve a bit of domain knowledge, references to journal papers, and deriving insights from them.

Warning: This post is nearly 10 minutes long and things may get a little dense as you scroll down, but I encourage you to give it a shot.

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About the data

The dataset was originally released by Ahmed et al., in 2017 [3] as a supplement to their analysis of survival of heart failure patients at Faisalabad Institute of Cardiology and at the Allied Hospital in Faisalabad, Pakistan. The dataset was subsequently accessed and analyzed by Chicco and Jurman in 2020 to predict heart failures using a bunch of machine learning techniques [4]. The dataset hosted on Kaggle cites these authors and their research paper.

The dataset primarily consists of clinical and lifestyle features of 105 female and 194 male heart failure patients. You can find each feature explained in the figure below.

Fig. 1 — Clinical and lifestyle features of 299 patients in the dataset (credit: author)

Project Workflow

The workflow would be pretty straightforward —

1. **Data Preprocessing — **Cleaning the data, imputing missing values, creating new features if needed, etc.
2. **Exploratory Data Analysis — **This would involve summary statistics, plotting relationships, mapping trends, etc.
3. **Model Building — **Building a baseline prediction model, followed by at least 2 classification models to train and test.

#heart-disease #data-science #machine-learning #exploratory-data-analysis #data-visualization

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Machine learning applications are a staple of modern business in this digital age as they allow them to perform tasks on a scale and scope previously impossible to accomplish.Businesses from different domains realize the importance of incorporating machine learning in business processes.Today this trending technology transforming almost every single industry ,business from different industry domains hire dedicated machine learning developers for skyrocket the business growth.Following are the applications of machine learning in different industry domains.

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• ML and AI can offer high security in the transportation industry.
• It offers high reliability of their services or vehicles.
• The adoption of this technology in the transportation industry can increase the efficiency of the service.
• In the transportation industry ML helps scientists and engineers come up with far more environmentally sustainable methods for powering and operating vehicles and machinery for travel and transport.

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Technology-enabled smart healthcare is the latest trend in the healthcare industry. Different areas of healthcare, such as patient care, medical records, billing, alternative models of staffing, IP capitalization, smart healthcare, and administrative and supply cost reduction. Hire dedicated machine learning developers for any of the following applications.

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• Drug Discovery and Manufacturing
• Medical Imaging Diagnosis
• Personalized Medicine
• Machine Learning-based Behavioral Modification
• Smart Health Records
• Clinical Trial and Research
• Better Radiotherapy
• Crowdsourced Data Collection
• Outbreak Prediction

**
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In financial industries organizations like banks, fintech, regulators and insurance are Adopting machine learning to improve their facilities.Following are the use cases of machine learning in finance.

• Fraud prevention
• Risk management
• Investment predictions
• Customer service
• Digital assistants
• Marketing
• Network security
• Loan underwriting
• Algorithmic trading
• Process automation
• Document interpretation
• Content creation
• Trade settlements
• Money-laundering prevention
• Custom machine learning solutions

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• Improved Cognitive Services
• Rise of Robots

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