Prediction of Heart Disease Using a Combination of Machine Learning and Deep Learning (2021-07-01T00:00:00.000000Z)

TL;DR

Different machine learning algorithms and deep learning are applied to compare the results and analysis of the UCI Machine Learning Heart Disease dataset and various promising results are achieved.

Abstract

The correct prediction of heart disease can prevent life threats, and incorrect prediction can prove to be fatal at the same time. In this paper different machine learning algorithms and deep learning are applied to compare the results and analysis of the UCI Machine Learning Heart Disease dataset. The dataset consists of 14 main attributes used for performing the analysis. Various promising results are achieved and are validated using accuracy and confusion matrix. The dataset consists of some irrelevant features which are handled using Isolation Forest, and data are also normalized for getting better results. And how this study can be combined with some multimedia technology like mobile devices is also discussed. Using deep learning approach, 94.2% accuracy was obtained.

Authors

R. Bharti

1 papers

A. Khamparia

1 papers

Mohammad Shabaz

1 papers

TL;DR

Abstract

Authors

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needed to be handled; otherwise, it will result in overﬁtting or underﬁtting of the data

(xii) Thal—no explanation provided, but probably thalassemia (3 normal; 6 ﬁxed defects; 7 reversible defects)

(vii) Thalach—maximum heart rate achieved. The maximum heart rate is 220 minus your age

(viii) Exang—exercise-induced angina (1 yes)

(iv) Chol—serum cholesterol shows the amount of triglycerides present. Triglycerides are another lipid that can be measured in the blood. It should be less than 170 mg/dL

(x) Slope—the slope of the peak exercise ST segment

ix) Oldpeak—ST depression induced by exercise relative to rest

(xi) Ca—number of major vessels (0–3) colored by ﬂuoroscopy

By analyzing the distribution plots, it is visible that thal and fasting blood sugar is not uniformly distributed and they

(v) Fbs—fasting blood sugar larger than 120 mg/dl (1 true). Less than 100 mg/dL (5.6 mmol/L) is normal, and 100 to 125 mg/dL (5.6 to 6.9 mmol/L) is considered prediabetes

Checking the features which are and not important for heart 5, respectively. Here the important

vi) Restecg—resting electrocardiographic results

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