What Is Data Analyticspart 1 Choosing A Dataseti Choose A Datas ✓ Solved

What is Data Analytics? Part 1: Choosing a Dataset: I choose a dataset from Kaggle. The name of my dataset is Stroke Prediction Dataset and my dataset consists of the following specifications: It has 12 columns and 5110 rows, and it is labeled data.

Part 2: Dataset Background Summary. According to the World Health Organization (WHO), stroke disease ranks second in causing deaths. Stroke may cause the deaths of many people; it is a disease in which blood supply is interrupted in a part of the brain. It prevents brain tissue from getting oxygen and nutrients. The brain cells begin to die within seconds. A stroke is a medical emergency that requires urgent treatment, with symptoms including high blood pressure, diabetes, and heart disease. These are major risk factors responsible for stroke attacks. We use these risk factors to predict stroke in machine learning. This dataset helps us determine whether the patient has suffered a stroke or not. The data comes from the electronic records of patients released by McKinsey & Company.

The electronic records indicate whether the patient suffers from heart disease, hypertension, etc., which helps to predict whether they have had a stroke. Many people suffer from stroke but do not realize it. Using machine learning algorithms, we can inform patients if they have suffered a stroke. These predictions can help patients understand their health better and address stroke risks efficiently.

Part 3: Dataset Info. This dataset contains the records of 5110 patients and 12 fields. It includes 11 input attributes and 1 output attribute. The input attributes are: id, age, gender, hypertension (binary attribute: 0 means the patient does not have hypertension, 1 means the patient has hypertension), heart disease (binary attribute: 0 means the patient does not have heart disease, 1 means the patient has heart disease), marital status, work type, residence type, average glucose level, body mass index (BMI), and smoking status. The output attribute is stroke, which predicts whether the patient had a stroke or not. The following table shows the column name, data type, and numeric or categorical types of data:

Column Name Data Type Numeric/Categorical

Id Int64 Numeric

Gender Object Categorical

Age Float64 Numeric

Hypertension Int64 Numeric

Heart Disease Int64 Numeric

Marital_Status Object Categorical

Work_Type Object Categorical

Residence_Type Object Categorical

Average_Glucose_Level Float64 Numeric

Body_Mass_Index Float64 Numeric

Smoking_Status Object Categorical

Stroke Int64 Numeric

Part 4: Import your Dataset. We use Jupyter Notebook to import this dataset by following some steps: First, we import the pandas library. Next, we use the function pd.read_csv() to import the dataset. Following that, we print the dataset using the head function. Finally, we describe the information of the dataset using the info() function, and we display the shape of the dataset as required.

Paper For Above Instructions

Data analytics is a crucial field that entails the process of analyzing raw data to identify trends, draw conclusions, and support decision-making. The contribution of data analytics spans healthcare, finance, business, and beyond, making it an integral tool in today's data-driven world. The Stroke Prediction Dataset selected from Kaggle offers significant insights into how data analytics can be applied to predict medical events, specifically stroke occurrences based on various patient attributes.

This dataset contains a wealth of information on 5110 patients, tracking 12 distinct attributes including demographics, clinical history, and lifestyle factors. The ability to leverage this data can greatly enhance our understanding of stroke risks and contribute to preventive healthcare strategies.

Stroke remains a leading cause of morbidity and mortality worldwide, necessitating a deeper understanding of its risk factors, symptoms, and prompt treatment approaches. According to the World Health Organization, stroke is responsible for a significant number of deaths globally. Understanding that stroke risks may be identified through various factors presents an opportunity to mitigate or prevent stroke occurrences by applying data analytics and machine learning methodologies.

The dataset covers multiple attributes critical for stroke prediction. For instance, age, gender, blood pressure, and lifestyle habits such as smoking status, all play significant roles in assessing a patient’s risk of stroke. The dataset leverages various predictive modeling techniques to enable analysis, ultimately predicting whether a stroke will occur by analyzing input variables. Ensuring accuracy in these predictions is paramount, as it can lead to timely assessments and interventions.

By extracting meaningful patterns from this dataset, healthcare providers can prioritize preventive measures among at-risk populations. Machine learning algorithms can be employed to evaluate potential correlations among various health indicators. For instance, conditional logic can assess how high blood pressure (hypertension) and body mass index (BMI) influence stroke likelihood. According to numerous studies, hypertension is recognized as one of the primary contributors to strokes. Hence, analytics that highlights this relationship can serve in policy-making for improved healthcare.

To analyze the Stroke Prediction Dataset, Jupyter Notebook serves as an effective platform. By cleaning and exploring the dataset, analysts can ensure that any outliers or missing values are addressed adequately. Furthermore, techniques such as data normalization are essential to place disparate value ranges on the same playing field, ensuring that model predictions are fair and equitable.

Following data importation using libraries such as Pandas in Python, analysts can utilize functions like head() and info() to preview data structure and characteristics. The descriptive analytics obtained help stakeholders quickly ascertain the dataset’s composition and structure. Moreover, generating visualizations through libraries like Matplotlib or Seaborn can deliver compelling graphical insights regarding variable correlations and trends over time.

In a broader context, the implications of predictive analytics in healthcare extend beyond stroke. Early identification of disease risk factors can lead to better health outcomes and allow for more tailored patient care. Additionally, predictive analytics can bolster public health strategies by identifying trends across populations, proving invaluable during public health crises, such as pandemics.

As we delve deeper into the analysis process, implementation of robust machine learning models such as logistic regression or decision trees allows for the development of predictive frameworks. Supervised learning approaches harness labeled data to improve model accuracy. Tools such as Scikit-learn can facilitate constructing these models while allowing practitioners to evaluate different classification strategies. Outcome assessments enable algorithms to classify model efficacy and modify strategies as necessary.

In summary, the Stroke Prediction Dataset offers a promising approach to utilizing data analytics in a healthcare domain plagued by significant mortality rates. By harnessing the power of data, we can push the boundaries of how we understand stroke risks and intricacies among patients. With continued advancements in machine learning and analytical tools, we can better configure healthcare landscapes while improving patient outcomes. The approach delineated not only sheds light on stroke prediction but also presents methods to enhance overall disease management in clinical settings.

References

  • World Health Organization. (2021). Global Health Estimates: Leading causes of death. Retrieved from https://www.who.int

  • McKinsey & Company. (2020). The role of data analytics in healthcare. Retrieved from https://www.mckinsey.com

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  • Khan, N., Nahar, N., & Rahman, R. (2020). Stroke Prediction Using Machine Learning Techniques: A Survey. Journal of Biomedical Engineering and Technology, 5(1), 10-18.

  • Johnson, A. E., et al. (2018). MIMIC-III, a freely accessible critical care database. Scientific Data, 3: 160035.

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  • Wang, W., Li, Y., & Zhang, R. (2019). Data Mining Approach for Predicting Stroke Risks. International Journal of Health Sciences, 13(6), 1-8.

  • Gunjan, D., & Alok, K. (2020). Machine Learning in Stroke Recognition. Journal of Biomedical Research & Therapeutics, 5(2), 131-138.

  • Bakhtiar, M. B., & Shakir, M. E. (2021). The Importance of Predictive Analytics in Healthcare – A Review. Journal of Health Management, 23(4), 602-614.