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Exercise heart health risk probability full#

It is implemented in Python and different classification algorithms are used.

Exercise heart health risk probability full#

The full code for this article can be found here.

Glucose: blood glucose level (Continuous)ġ0 year risk of developing coronary heart disease (CHD) - (binary: “1”, means “There is a risk”, “0” means “There is no risk”) 3.

Heart Rate: heart rate (Continuous - In medical research, variables such as heart rate though in fact discrete, yet are considered continuous because of large number of possible values.).

Dia BP: diastolic blood pressure (Continuous).

Sys BP: systolic blood pressure (Continuous).

Tot Chol: total cholesterol level (Continuous).

Information on current medical condition:

Diabetes: whether or not the patient had diabetes (Nominal)ĥ.

Prevalent Hyp: whether or not the patient was hypertensive (Nominal).

Prevalent Stroke: whether or not the patient had previously had a stroke (Nominal).

BP Meds: whether or not the patient was on blood pressure medication (Nominal).

Cigs Per Day: the number of cigarettes that the person smoked on average in one day.(can be considered continuous as one can have any number of cigarettes, even half a cigarette.).

Current Smoker: whether or not the patient is a current smoker (Nominal).

Education: no further information provided

Age: Age of the patient (Continuous - Although the recorded ages have been truncated to whole numbers, the concept of age is continuous)Ģ.

There are both demographic, behavioral and medical risk factors. Each attribute is a potential risk factor.

It includes over 4,000 records and 15 attributes. The data set provides the patients’ information. The classification goal is to predict whether the patient has 10-year risk of future coronary heart disease (CHD). The data set is publicly available on the Kaggle website, and it is from an ongoing cardiovascular study on residents of the town of Framingham, Massachusetts. The code for this article can be found in my Github repository or forked directly from its companion Kaggle notebook 2. In this article, I will be giving you a walk through on the development of a screening tool for predicting whether a patient has 10-year risk of developing coronary heart disease(CHD) using different Machine Learning techniques on the Framingham dataset. This is where machine learning and data mining come to the rescue.ĭoctors and scientists alike have turned to machine learning (ML) techniques to develop screening tools and this is because of their superiority in pattern recognition and classification as compared to other traditional statistical approaches. It is, however, difficult to identify high risk patients because of the multi-factorial nature of several contributory risk factors such as diabetes, high blood pressure, high cholesterol, et cetera. The silver lining is that heart attacks are highly preventable and simple lifestyle modifications(such as reducing alcohol and tobacco use eating healthily and exercising) coupled with early treatment greatly improves its prognosis. In the United States, for example, it is estimated that someone has a heart attack every 40 seconds and about 805,000 Americans have a heart attack every year ( CDC 2019). Of all heart diseases, coronary heart disease (aka heart attack) is by far the most common and the most fatal.

Over three quarters of these deaths took place in low- and middle-income countries. According to the WHO, an estimated 17.9 million people died from heart disease in 2016, representing 31% of all global deaths. Heart disease is the major cause of morbidity and mortality globally: it accounts for more deaths annually than any other cause.