In today's generation, many people face heart-related diseases and lose their life due to heart attack, stroke, and cardiac arrest. Heart strokes have become a challenging symptom as patients face issues like speech difficulty, face drooping and arm weakness which shows the patient is suffering from heart-related issues. To prevent the deaths due to heart strokes, the new technologies could help to predict the symptoms earlier, which are responsible for heart-related diseases and reduce the number of deaths. The chapter also shows the comparison of different algorithms to differentiate from old traditional methodologies and find the best methodology which gives the best result. In the case of prediction models, parameters like age, gender, BMI, medical history, drinking and smoking habits, level of glucose, type of work the person does, etc., which will help to find the root cause for deaths in heart-related diseases. The use of prediction models, algorithms like random forest, decision tree, k-nearest neighbour, SVM, help to make a comparative analysis and keep a record of accurate models.
Top1. Introduction
According to a survey over 3 million individuals lose their lives due to heart attacks and strokes assault per year. Heart stroke is found to be a dominant reason that loses their life due to heart-related diseases. To overcome the issues of death due to heart stroke, a predictive modelling approach can be used to collect the data with multiple methods that find the root causes of heart stroke which will help to reduce the deaths due to heart stroke. By using the method many people can be saved and treated to avoid heart-related diseases. Overall, the price of equipment which is used today for the detection of heart-related diseases is generally very expensive and not possible for everyone to buy. Also, high blood pressure is found to be one more cause of heart-stroke disease. By using the method of automation of new generation technology the problem can be reduced and help many people to cure heart-related issues. Machine learning is played as an important technology to solve the issues related to heart diseases. With the aid of technological development, people with heart illnesses can also be identified and their likelihood of survival predicted, potentially saving many lives. In the case of a prediction model, it is also necessary to take into consideration factors like age, gender, bmi, other illnesses if present, medical history, drinking and smoking habits, level of glucose, type of work the person performs, etc. that will aid in determining the underlying cause of deaths from heart-related diseases. With the aid of a prediction model, algorithms and approaches such as Decision trees, K-Nearest Neighbour random forest, SVM (Support Vector Machine), and others assist in making comparisons and maintaining records of model correctness. By utilising several models and keeping records, it will be possible to build the therapeutic setting's guiding principles.
Since a few years ago, the prevalence of cardiovascular disorders has been rising quickly over the globe. Even though these diseases have been determined to be the leading reason for death, they have also been identified as the most controllable and preventable diseases. Cholesterol is the main cause of heart attacks. When the heart is unable to efficiently pump blood throughout the body, it occurs..
One of the major contributing factors to developing heart disease is high blood pressure. According to a report, 35% of people worldwide had hypertension between 2011 and 2014, This raises the risk of heart disease. other factors can lead to Arterial blockage is the main cause of heart attacks. It takes place when the body's blood cannot be properly pumped by the heart. a lack of exercise. Understanding cardiac disorders for prevention, is essential. The fact that almost 47% of fatalities take place outside of hospitals shows how frequently warning signs are ignored.
Heart disorders today shorten a person's life expectancy. To achieve its goals for preventing non-communicable diseases (NCDs), the World Health Organization (WHO) adopted targets in 2013. By 2025, at least 50% of cardiovascular patients illnesses are anticipated to have operate to the right medications and medical suggestion. In 2016, 17.9 million fatalities worldwide—or 31% of all deaths—were directly related to cardiovascular disease.
A key challenge is identifying heart issues. It might be difficult to tell if someone has a cardiac issue or not. Although there are technologies that can forecast heart disease, they either cost a lot of money or are inefficient at determining how likely heart disease is to occur in humans. There has been extensive research done in this field because, based on a World Health Organization (WHO) report, only 67% of cardiac diseases can be predicted by medical professionals. In rural areas of India, there is a serious lack of access to hospitals and high-quality medical care. Only 58% of doctors in urban areas and 19% in rural areas have medical degrees, according to a 2016 WHO report.