Course Content

Abstract :

The application of machine learning in the field of medical diagnosis is increasing gradually. This can be contributed primarily to the improvement in the classification and recognition systems used in disease diagnosis which is able to provide data that aids medical experts in early detection of fatal diseases and therefore, increase the survival rate of patients significantly.

Heart disease is the Leading cause of death worldwide. With the rampant increase in the heart stroke rates at juvenile ages, we need to put a system in place to be able to detect the symptoms of a heart stroke at an early stage and thus prevent it. It is impractical for a common man to frequently undergo costly tests like the ECG and thus there needs to be a system in place which is handy and at the same time reliable, in predicting the chances of a heart disease.

So we proposed a system with the help of machine learning techniques and algorithms like Logistic Regression, KNN, SVC, Random Forest ,Decision Tree , XGB Classifier and  Naïve Bayes to predict Heart Disease based on different parameters entered by the user in the front end.

Introduction:

Due to busy schedule as well as routine assignments peoples are facing severe stress and anxiety. More over some other peoples are addicted with chronic habitual behaviour, like consumption of Cigars and Gutuka, those peoples are suffering from chronic diseases like, heart diseases, cancer, Liver problems, Kidney failures etc. To cure such persons with chronic disease is a big hurdle to well know doctors, is a current world issue. Regarding this new challenge, IT professionals are provided hand to hand support to predict such disease early and cure as well as recover the patients from the chronic disease.

In the present scenario each humans are so exceptional in his individual features and manners, but even though every humans may have different pulse rate as well as blood pressure ratings. Based on the history and generic evaluation of medical practitioners and researchers believed that, a healthy humans pulse rate is varied in between of 60 to 100 bpm and BP is varied in between of 120/80 to 140/90 (mm Hg), and these readings are proved by medical practitioners. Heart syndrome is one the vital abrupt death or accidental death of humans in this world, this is might be because of poor dieting as well as physical exercise and other activities like, consumption of alcoholic products, smoking etc. In this article, author is tried to predict and analysis the heart syndrome with respect to many features like age, gender, blood pressure, heart rate, diabetes etc, but however actual prediction of heart syndrome is totally a critical task to the medical practitioners and analyst. In present market, health industries has many machine learning tools and techniques are used to predict various chronic diseases, but still researchers find some sort of flaws, so they expect some more effective and efficient predictive algorithms to find chronic diseases of humans in early stage itself, so that we can save the life of the patients.

So we proposed a system with the help of machine learning techniques and algorithms like Logistic Regression, KNN, SVC, Random Forest ,Decision Tree , XGB Classifier and  Naïve Bayes to predict Heart Disease based on different parameters entered by the user in the front end.

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Objective:

The main aim of this project to predict the Heart Disese using machine learning techniques and algorithms like like Logistic Regression, KNN, SVC, Random Forest ,Decision Tree, XGB Classifier and  Naïve Bayes based on different parameters entered by the user in the front end.

Problem Statement

Predicting and detection of heart disease has always been a critical and challenging task for healthcare practitioners. Hospitals and other clinics are offering expensive therapies and operations to treat heart diseases. So, predicting heart disease at the early stages will be useful to the people around the world so that they will take necessary actions before getting severe.

 

 

Proposed System:

 

We proposed a system with the help of machine learning techniques and algorithms like Logistic Regression, KNN, SVC, Random Forest ,Decision Tree , XGB Classifier and  Naïve Bayes to predict Heart Disease based on different parameters entered by the user in the front end.