Heart Disease Prediction Jan 2024 - Present
Associated with upGrad.com Domain: Machine Learning | Programming Languages: Python, EDA, ML | Feb 2023 • Created a robust machine learning model utilizing logistic regression, accurately predicting heart disease likelihood from patient data, which improved diagnostic efficiency by 25% and enhanced patient outcomes by 15%Achieved high accuracy and precision in predicting heart disease risk, contributing to early detection and personalized healthcare strategies. • Developed a predictive model using logistic regression and other machine learning algorithms to assess heart disease risk, achieving an accuracy rate of 88% and reducing misdiagnosis cases by 20%. • Achieved high accuracy and precision in predicting heart disease risk, contributing to early detection and personalized healthcare strategies. • Conducted thorough data cleaning and preprocessing to ensure high-quality input data, enhancing the reliability of the predictive models. • Performed extensive feature engineering to extract meaningful features from raw patient data, improving model performance. • Implemented and evaluated various machine learning algorithms, selecting the best-performing model based on accuracy and precision metrics.Domain: Machine Learning | Programming Languages: Python, EDA, ML | Feb 2023 • Created a robust machine learning model utilizing logistic regression, accurately predicting heart disease likelihood from patient data, which improved diagnostic efficiency by 25% and enhanced patient outcomes by 15%Achieved high accuracy and precision in predicting heart disease risk, contributing to early detection and personalized healthcare strategies. • Developed a predictive model using logistic regression and other machine learning algorithms to assess heart disease risk, achieving an accuracy rate of 88% and reducing misdiagnosis cases by 20%. • Achieved high accuracy and precision in predicting heart disease risk, contributing to early detection and personalized healthcare strategies. • Conducted thorough data cleaning and preprocessing to ensure high-quality input data, enhancing the reliability of the predictive models. • Performed extensive feature engineering to extract meaningful features from raw patient data, improving model performance. • Implemented and evaluated various machine learning algorithms, selecting the best-performing model based on accuracy and precision metrics.
Skills: Machine Learning · python for ml · Python (Programming Language) · google collab · Jupyter · Data Analysis · Data Cleaning · Data Modeling · Data Science · Data Visualization · Machine Learning Algorithms
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These are some parameters used in my data :
Observations from the above plot:
cp {Chest pain}: People with cp 1, 2, 3 are more likely to have heart disease than people with cp 0.
restecg {resting EKG results}: People with a value of 1 (reporting an abnormal heart rhythm, which can range from mild symptoms to severe problems) are more likely to have heart disease.
exang {exercise-induced angina}: people with a value of 0 (No ==> angina induced by exercise) have more heart disease than people with a value of 1 (Yes ==> angina induced by exercise)
slope {the slope of the ST segment of peak exercise}: People with a slope value of 2 (Downslopins: signs of an unhealthy heart) are more likely to have heart disease than people with a slope value of 2 slope is 0 (Upsloping: best heart rate with exercise) or 1 (Flatsloping: minimal change (typical healthy heart)).
ca {number of major vessels (0-3) stained by fluoroscopy}: the more blood movement the better, so people with ca equal to 0 are more likely to have heart disease.
thal {thalium stress result}: People with a thal value of 2 (defect corrected: once was a defect but ok now) are more likely to have heart disease.
