Given clinical parameters about a patient, need to identify whether a person has heart disease or not.
Heart disease describes a range of conditions that affect your heart. Diseases under the heart disease umbrella include blood vessel diseases, such as coronary artery disease, heart rhythm problems (arrhythmias) and heart defects you’re born with (congenital heart defects), among others.
The term heart disease is often used interchangeably with the term cardiovascular disease. Cardiovascular disease generally refers to conditions that involve narrowed or blocked blood vessels that can lead to a heart attack, chest pain (angina) or stroke. Other heart conditions, such as those that affect your heart’s muscle, valves or rhythm, also are considered forms of heart disease.
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.
According to a news article, heart disease proves to be the leading cause of death for both women and men. The article states the following :
- About 610,000 people die of heart disease in the United States every year–that’s 1 in every 4 deaths.1
- Heart disease is the leading cause of death for both men and women. More than half of the deaths due to heart disease in 2009 were in men.1
- Coronary Heart Disease(CHD) is the most common type of heart disease, killing over 370,000 people annually.
- Every year about 735,000 Americans have a heart attack. Of these, 525,000 are a first heart attack and 210,000 happen in people who have already had a heart attack.
The World Health Organization (WHO) lists cardiovascular diseases as the leading cause of death globally with 17.9 million people dying every year. The risk of heart disease increases due to harmful behavior that leads to overweight and obesity, hypertension, hyperglycemia, and high cholesterol. Furthermore, the American Heart Association complements symptoms with weight gain (1–2 kg per day), sleep problems, leg swelling, chronic cough and high heart rate. Diagnosis is a problem for practitioners due the symptoms’ nature of being common to other conditions or confused with signs of aging.
This makes heart disease a major concern to be dealt with. But it is difficult to identify heart disease because of several contributory risk factors such as diabetes, high blood pressure, high cholesterol, abnormal pulse rate, and many other factors. Due to such constraints, scientists have turned towards modern approaches like Data Mining and Machine Learning for predicting the disease.
Given clinical parameters about a patient, need to identify whether a person has heart disease or not.
The dataset consists of 303 individuals data. There are 14 columns in the dataset, which are described below.
1. Age : displays the age of the individual.
2. Sex : displays the gender of the individual using the following format :
1 = male
0 = female
3. Chest-pain type : displays the type of chest-pain experienced by the individual using the following format :
1 = typical angina
2 = atypical angina
3 = non — anginal pain
4 = asymptotic
4. Resting Blood Pressure : displays the resting blood pressure value of an individual in mmHg (unit)
5. Serum Cholestrol : displays the serum cholesterol in mg/dl (unit)
6. Fasting Blood Sugar : compares the fasting blood sugar value of an individual with 120mg/dl.
If fasting blood sugar > 120mg/dl then : 1 (true)
else : 0 (false)
7. Resting ECG : displays resting electrocardiographic results
0 = normal
1 = having ST-T wave abnormality
2 = left ventricular hyperthrophy
8. Max heart rate achieved : displays the max heart rate achieved by an individual.
9. Exercise induced angina :
1 = yes
0 = no
10. ST depression induced by exercise relative to rest : displays the value which is an integer or float.
11. Peak exercise ST segment :
1 = upsloping
2 = flat
3 = downsloping
12. Number of major vessels (0–3) colored by flourosopy : displays the value as integer or float.
13. Thal : displays the thalassemia :
3 = normal
6 = fixed defect
7 = reversible defect
14. Diagnosis of heart disease : Displays whether the individual is suffering from heart disease or not :
0 = absence
1, 2, 3, 4 = present.
In the actual dataset, we had 76 features but for our study, we chose only the above 14 because :
1. Age : Age is the most important risk factor in developing cardiovascular or heart diseases, with approximately a tripling of risk with each decade of life. Coronary fatty streaks can begin to form in adolescence. It is estimated that 82 percent of people who die of coronary heart disease are 65 and older. Simultaneously, the risk of stroke doubles every decade after age 55.
2. Sex : Men are at greater risk of heart disease than pre-menopausal women. Once past menopause, it has been argued that a woman’s risk is similar to a man’s although more recent data from the WHO and UN disputes this. If a female has diabetes, she is more likely to develop heart disease than a male with diabetes.
3. Angina (Chest Pain) : Angina is chest pain or discomfort caused when your heart muscle doesn’t get enough oxygen-rich blood. It may feel like pressure or squeezing in your chest. The discomfort also can occur in your shoulders, arms, neck, jaw, or back. Angina pain may even feel like indigestion.
4. Resting Blood Pressure : Over time, high blood pressure can damage arteries that feed your heart. High blood pressure that occurs with other conditions, such as obesity, high cholesterol or diabetes, increases your risk even more.
5. Serum Cholesterol : A high level of low-density lipoprotein (LDL) cholesterol (the “bad” cholesterol) is most likely to narrow arteries. A high level of triglycerides, a type of blood fat related to your diet, also ups your risk of a heart attack. However, a high level of high-density lipoprotein (HDL) cholesterol (the “good” cholesterol) lowers your risk of a heart attack.
6. Fasting Blood Sugar : Not producing enough of a hormone secreted by your pancreas (insulin) or not responding to insulin properly causes your body’s blood sugar levels to rise, increasing your risk of a heart attack.
7. Resting ECG : For people at low risk of cardiovascular disease, the USPSTF concludes with moderate certainty that the potential harms of screening with resting or exercise ECG equal or exceed the potential benefits. For people at intermediate to high risk, current evidence is insufficient to assess the balance of benefits and harms of screening.
8. Max heart rate achieved : The increase in cardiovascular risk, associated with the acceleration of heart rate, was comparable to the increase in risk observed with high blood pressure. It has been shown that an increase in heart rate by 10 beats per minute was associated with an increase in the risk of cardiac death by at least 20%, and this increase in the risk is similar to the one observed with an increase in systolic blood pressure by 10 mm Hg.
9. Exercise induced angina : The pain or discomfort associated with angina usually feels tight, gripping or squeezing, and can vary from mild to severe. Angina is usually felt in the center of your chest but may spread to either or both of your shoulders, or your back, neck, jaw or arm. It can even be felt in your hands. o Types of Angina a. Stable Angina / Angina Pectoris b. Unstable Angina c. Variant (Prinzmetal) Angina d. Microvascular Angina.
10. Peak exercise ST segment : A treadmill ECG stress test is considered abnormal when there is a horizontal or down-sloping ST-segment depression ≥ 1 mm at 60–80 ms after the J point. Exercise ECGs with up-sloping ST-segment depressions are typically reported as an ‘equivocal’ test. In general, the occurrence of horizontal or down-sloping ST-segment depression at a lower workload (calculated in METs) or heart rate indicates a worse prognosis and higher likelihood of multi-vessel disease. The duration of ST-segment depression is also important, as prolonged recovery after peak stress is consistent with a positive treadmill ECG stress test. Another finding that is highly indicative of significant CAD is the occurrence of ST-segment elevation > 1 mm (often suggesting transmural ischemia); these patients are frequently referred urgently for coronary angiography.
https://archive.ics.uci.edu/ml/datasets/heart+disease
https://www.kaggle.com/ronitf/heart-disease-uci
