We will predict the chance of heart failure by applying machine learning techniques. The work is to design and develop models to predict heart failure rate. With the advanced development in machine learning (ML) artificial intelligence (AI) and data science has been shown to be effective in assisting in decision making and predictions from the large quantity of data produced by the healthcare industry.
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Here're some of the project's best features:
- INTRODUCTION Cardiovascular disorders(CVDs) are a common cause of heart failure. People who have cardiovascular disease or are at high cardiovascular risk due to one or more risk factors such as hypertension diabetes hyperlipidemia or previously existing disease require early detection and management which a machine learning model can provide.
- DATA SET DESCRIPTION The classification goal is to predict whether the patient has Cardiovascular disorders(CVDs) or not. The data set provides the patients’ information. It includes 918 records and 11 attributes. Each attribute is a potential risk factor. There are both demographic and medical risk factors.
- Attributes: Demographic: Age: age of the patient [years] Sex: sex of the patient [M: Male F: Female] Information on medical records: ChestPainType: chest pain type [TA: Typical Angina ATA: Atypical Angina NAP: Non-Anginal Pain ASY: Asymptomatic] RestingBP: resting blood pressure [mm Hg] Cholesterol: serum cholesterol [mm/dl] FastingBS: fasting blood sugar [1: if FastingBS > 120 mg/dl 0: otherwise] RestingECG: resting electrocardiogram results [Normal: Normal ST: having ST-T wave abnormality (T wave inversions and/or ST elevation or depression of > 0.05 mV) LVH: showing probable or definite left ventricular hypertrophy by Estes' criteria] MaxHR: maximum heart rate achieved [Numeric value between 60 and 202] ExerciseAngina: exercise-induced angina [Y: Yes N: No] Oldpeak: oldpeak = ST [Numeric value measured in depression] ST_Slope: the slope of the peak exercise ST segment [Up: upsloping Flat: flat Down: downsloping]
- Model development and comparison: We used five classification models i.e. Random Forest Classifier K-Neighbors Decision Trees XGboost Classifier and LightGBM Classifier After which we compared the performance of the models using their accuracy and F1 score. EXPERIMENTATION Model Development And Comparison: Using the training set we trained the above-mentioned five classifiers after training each model and tuning their hyper-parameters using random search we evaluated and compared their performance using the Accuracy score F1 Score Precision Score and ROC as follows: Random Forest Classifier
Technologies used in the project:
- Python
- Machine learning Models