Project PPT(N) for a good buiness development ND BITCOIN.pptx

SCIENCE PROJECT FOR MCA STUDENTS

1. NOBLE INSTITUTE OF SCIENCE AND TECHNOLOGY DEPARTMENT OF COMPUTER SCIENCE PREDICTION OF CARDIAC DISEASE USING MACHINE LEARNING AREA OF THE PROJECT: MACHINE LEARNING Submitted By Gantla Nagendra Registered numbers 322225620016 Project Guide: P.Kavitha

2. ABSTRACT The healthcare industry is dealing with billions of patients all over the world and producing massive data. The machine learning-based models are dissecting the multidimensional medical datasets and generating better insights. In this study, a cardiovascular dataset is classified by using several state-of-the-art Supervised Machine Learning algorithms that are precisely used for disease prediction. The results indicate that the Decision Tree classification model predicted the cardiovascular diseases better than Naive Bayes, Logistic Regression, Random Forest, SVM and KNN based approaches. This approach could be helpful for doctors to predict the occurrence of heart diseases in advance and provide appropriate treatment.

3. PROBLEM STATEMENT The major challenge in heart disease is its detection. There are instruments available which can predict heart disease but either it are expensive or are not efficient to calculate chance of heart disease in human. Early detection of cardiac diseases can decrease the mortality rate and overall complications. However, it is not possible to monitor patients everyday in all cases accurately and consultation of a patient for 24 hours by a doctor is not available since it requires more sapience, time and expertise. Since we have a good amount of data in today's world, we can use various machine learning algorithms to analyze the data for hidden patterns. The hidden patterns can be used for health diagnosis in medicinal data.

4. EXISTING SYSTEM In efficient Cardiac disease prediction has been made by using various algorithms some of them include Logistic Regression, KNN, Random Forest Classifier Etc. It can be seen in results that each algorithm has its strength to register the defined objectives. The model incorporating IHDPS(Intelligent Heart Disease Prediction System) had the ability to calculate the decision boundary using the previous and new model of machine learning and deep learning. It facilitated the important and the most basic factors/knowledge such as family history connected with any heart disease. But the accuracy that was obtained in such IHDPS model was far more less. DRAWBACKS • Low accuracy • Unwanted data downs efficiency

5. PROPOSED SYSTEM The working of the system starts with the collection of data and selecting the important attributes. Then the required data is preprocessed into the required format. The data is then divided into two parts training and testing data. The algorithms are applied and the model is trained using the training data. The accuracy of the system is obtained by testing the system using the testing data. This system is implemented using the following modules. ✓ Collection of Dataset ✓ Selection of attributes ✓ Data Pre-Processing ✓ Balancing of Data ✓ Disease Prediction

6. DESIGN

7. DESIGN

8. MODULES COLLECTION OF DATA : Data collection allows you to capture a record of past events so that we can use data analysis to find recurring patterns. From those patterns, you build predictive models using machine learning algorithms that look for trends and predict future changes. SELECTION OF ATTRIBUTES: Attribute selection is also defined as “the process of finding a best subset of features, from the original set of features in a given data set, optimal according to the defined goal and criterion of feature selection (a feature goodness criterion)”. DATA PRE-PROCESSING : Data preprocessing, a component of data preparation, describes any type of processing performed on raw data to prepare it for another data processing procedure. It has traditionally been an important preliminary step for the data mining process.

9. BALANCING OF DATA:        A balanced dataset is a dataset where each output class (or target class) is represented by the same number of input samples. Balancing can be performed by exploiting one of the following techniques: oversampling, undersampling , class weight. DISEASE PREDICTION: The disease prediction model involves three modules: (a) data cleaning, (b) feature extraction, and (c) classification. 2. Firstly, the two heart disease datasets, “Cleveland and Statlog ” and a breast cancer dataset known as Wisconsin Breast Cancer (WBC) were attained from the UCI data repository.

10. CONCLUSION This Heart Disease detection system assists a patient based on his/her clinical information of them been diagnosed with a previous heart disease. With the rising number of deaths due to heart disease, it is becoming increasingly important to build a system that can effectively and accurately forecast heart disease. The motivation for the study was to find the most efficient ML algorithm for detection of heart diseases. This study compares the accuracy score of KNN, Logistic Regression, Decisiontree , NaïveBayes , SVM and Random Forest for predicting heart disease using machine learning repository dataset.

11.        THANK YOU