heart disease predction using machiine learning
- 1. Kallam Harandha Reddy Institute Of Technology (Autonomous)
- 2. Heart Disease Prediction System using Exploratory Data Analysis Mentored By- Mr.Nagarjuna Submitted By- P.Pravallika(CSE/576) N.Divya(CSE/570) Y.Joshua(CSE/5A6) N.MukeshKrishna(CSE/569)
- 3. TABLE OF CONTENTS: 1. Abstract 2. Introduction 3. Existing System 4. Proposed System 5. System Specifications 6. System Architecture 7. Problem Statement 8. Data Collection 9. Technology Used 10. Data Cleaning 11. Library Used 12. Tableau Dashboard 13. Conclusion
- 4. Abstract ● With an opulence of data , healthcare is being developed by the application of machine learning. ● Cardiovascular disease is one of the most fatal conditions in the present world. In case of heart diseases, the correct diagnosis in early stage is important as time is the very important factor. ● We are building a Heart Disease Prediction system to predict the chance of heart disease , for this we use different algorithms like Logistic Regression and Random Forests by giving age, gender, blood pressure etc as input. As a output it gives the chance of getting heart disease.
- 5. Introduction Heart disease predictor is an offline platform designed and developed to explore the path of machine learning . The goal is to predict the health of a patient from collective data, so as to be able to detect configurations at risk for the patient, and therefore, in cases requiring emergency medical assistance, alert the appropriate medical staff of the situation of the latter. We initially have a dataset collecting information of many patients with which we are able to conclude the results into a complete form and can predict data precisely. The results of the predictions, derived from the predictive models generated by machine learning, will be presented through several distinct graphical interfaces according to the datasets considered. We will then bring criticism as to the scope of our results.
- 6. Existing System ● Diagnosis of the disease solely depends upon the docter’s intuition and patient’s records. ● Researchers made use of several data mining techniques that are accessible to help the specialists or physicians identify the heart disease. One of them is Naïve Bayes algorithm. ● The disadvantages of this prediction are, cardiovascular disease results are not accurate , cannot handle enormous datasets for patient records. Disadvantages: ● This practice leads to unwanted biases ,errors and excessive medical costs which effects the quality of service provided to patients. ● There are many ways that a medical misdiagnosis can represent itself .
- 7. Proposed System ● Machine learning techniques are used to increase the accuracy rate. ● In machine learning technique we can use the following algorithms on huge datasets to predict the heart disease. ● 1.Logistic Regression ● 2.Random Forest ● Logistic Regression algorithm is used to improve the accuracy of the system. ● By using this ,the proposed system acts as a decision support system and will predict the chances of heart diseases.
- 8. System Specifications Software requirements: ● OS : Windows ● Python IDE : Python 2.7.x and above Anaconda IDE ● Setup tools and pip to be installed for 3.6.x and above Hardware requirements: ● RAM : 4GB and Higher ● Processor : Intel i3 and above ● Hard Disk : 500GB: Minimum
- 10. Problem Statement Machine learning allows building models to quickly analyze data and deliver results, leveraging the historical and real-time data, with machine learning that will help healthcare service providers to make better decisions on patient’s disease diagnosis. By analyzing the data we can predict the occurrence of the disease in our project. This intelligent system for disease prediction plays a major role in controlling the disease and maintaining the good health status of people by predicting accurate disease risk. Machine learning algorithms can also be helpful in providing vital statistics, real-time data and advanced analytics in terms of the patient’s disease, lab test results, blood pressure, family history, clinical trial data, etc., to doctors.
- 11. Data Collection Data has been collected from Kaggle. Data collection is the process of gathering and Measuring information from countless different sources. In order to use the datawe collect to develop practical Artificial Intelligence (AI) amd Machine learning solutions it must be collected and stored in a way that makes sense for the business problem at hand What is Kaggle? KAGGLE is an online community of data scientists and machine learners, owned by Google LLC. Kaggle allows users to find and publish data sets, explore and build models in a web based data science environment, work with other data scientists and other machine learning engineers and enter data competitions to solve data science challenges
- 12. Attributes in Dataset ● Age ● Sex ● Chest Pain (CP) ● Blood Pressure (Trestbps) ● Cholestrol (Chol)Major ● Fasting Blood Sugar (fbs) ● Heart Rate (Thalach) ● Resting electrocardiographic results(Rest ● Exercise induced angina (Exang) ● Expression (oldpeak) ● Slope ● vessels (Ca)
- 13. Testing Technologies Anaconda(Python) - Anaconda is a free and open-source distribution of the Python and R programming languages for scientific computing, that aims to simplify package management and deployment. Jupyter Notebook - The Jupyter Notebook is an open-source web application that allows you to create and share documents that contain live code, equations, visualizations and narrative text. Uses include: data cleaning and transformation, numerical simulation, statistical modeling, data visualization, machine learning, and much more.
- 14. Data Cleaning Data Cleaning is essentially the task of removing errors and anomalies or replacing observed values with the true values from data to get more values in analytics . METHODS ● Get Rid of Extra Spaces. ● Select and Treat All Blank Cells. ● Convert Numbers Stored as Text into Numbers. ● Remove Duplicates. ● Highlight Errors. ● Change Text to Lower/Upper/Proper Case. ● Spell Check. ● Delete all Formatting.
- 15. Libraries Used 1. Pandas- is a software library written for the Python programming language for data manipulation and analysis. In particular, it offers data structures and operations for manipulating numerical tables and time series pandas is a Python package providing fast, flexible, and expressive data structures designed to make working with “relational” or “labeled” data both easy and intuitive. It aims to be the fundamental high-level building block for doing practical, real world data analysis in Python. 2. .NumPy- NumPy is a library for the Python programming language, adding support for large, multi-dimensional arrays and matrices, along with a large collection of high-level mathematical functions to operate on these arrays.
- 16. Tableau Dashboard ● Tableau is one of the business intelligence software used to analyse data and visualize the insights in the form of graph and charts. ● User can develop and share an interactive dashboard which shows the hidden pattern, trends, density and variation of data. ● Tableau uses centroid-based k-means clustering algorithm that divides the data into K-number of clusters. ● Dashboards are created with the data set after applying K-means algorithm. ● It provides visual appealing clusters in order to predict the occurrence of heart disease from the given dataset.
- 18. Conclusion ● The models we used to predict the probability of having heart disease are Logistic regression,Random forest as they are more accurate in numerical variables. The model accuracy is 85 % in test and train data sets. This model will would be used in medical field as it can predict the heart diseases . ● Heart stroke and vascular disease are the major cause of disability and premature death. Chest pain is the key to recognize the heart disease. In this work, the heart diseases are predicted by considering major factors with four types of chest pain. K-means clustering is one of the simplest and popular unsupervised machine learning algorithms. Here the datasets is clustered and based upon the clusters the happening of chest pain is predicted. The role of exploratory data using tableau provided a visual appealing and accurate clustering experience. 18
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