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heart-disease-predication icon heart-disease-predication

Machine learning (ML) proves to be effective in assisting in making decisions and predictions from the large quantity of data produced by the healthcare industry .The people’s age who are suffering from the disease or not. Here, target = 1 implies that the person is suffering from heart disease and target = 0 implies the person is not suffering. The dataset contains 14 columns and 303 rows.

heart-disease-prediction icon heart-disease-prediction

Heart disease is one of the biggest causes of morbidity and mortality among the population of the world. Prediction of cardiovascular disease is regarded as one of the most important subjects in the section of clinical data analysis. The amount of data in the healthcare industry is huge. Data mining turns the large collection of raw healthcare data into information that can help to make informed decisions and predictions.

hearthealthprediction icon hearthealthprediction

The major reason for the death in worldwide is the heart disease in high and low developed countries. The data scientist uses distinctive machine learning techniques for modeling health diseases by using authentic dataset efficiently and accurately. The medical analysts are needy for the models or systems to predict the disease in patients before the strike. High cholesterol, unhealthy diet, harmful use of alcohol, high sugar levels, high blood pressure, and smoking are the main symptoms of chances of the heart attack in humans. Data Science is an advanced and enhanced method for the analysis and encapsulation of useful information. The attributes and variable in the dataset discover an unknown and future state of the model using prediction in machine learning. Chest pain, blood pressure, cholesterol, blood sugar, family history of heart disease, obesity, and physical inactivity are the chances that influence the possibility of heart diseases. This project emphasizes to evaluate different algorithms for the diagnosis of heart disease with better accuracies by using the patient’s data set because predictions and descriptions are fundamental objectives of machine learning. Each procedure has unique perspective for the modeling objectives. Algorithms have been implemented for the prediction of heart disease with our Heart patient data set

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