Uncovering Healthcare Insights with AI Data Analysis

To get started with uncovering your healthcare insights, import your data into Vizly. Vizly can accept CSV, Excel, or JSON files, and can import data from Google Sheets. For more instructions on how to get started, check out our Getting Started guide.

We will be using a mental health dataset for this example.

After your data is successfully imported, you can begin your conversation with Vizly. Automated suggestions are provided to you in case you need a place to start.

To get an initial understanding of the data, we will ask Vizly for a correlation matrix of the different features in the dataset.

Vizly is able to understand the correlation between different features in our mental health dataset, and display an interactive correlation matrix.

A moderate positive correlation of 0.37 between treatment and family history in the mental health dataset suggests that individuals who are aware of their mental health issues through family history might be more proactive in seeking treatment due to increased awareness and reduced stigma around these conditions. This correlation highlights the potential impact of familial awareness and the changing perceptions of mental health on treatment-seeking behaviors.

To further explore how family history affects awareness of mental health care options, we can ask Vizly to dive deeper:

Vizly can generate tables, visualizations, and more.

We can see a clear pattern: 41.43% of individuals with a family history are aware of care options, compared to only 27.06% without. This significant difference underscores the impact of personal and familial experiences on mental health resource engagement.

Vizly can be used to forecast future trends in your data. In the context of mental health, Vizly can specifically predict the likelihood that individuals will seek treatment based on factors such as family history, enhancing proactive interventions and personalized care strategies.

Vizly can clean data, train models, and predict future trends.

Vizly has trained a Random Forest Classifer model that can accurately predict whether a person is likely to seek mental health treatment based on their survey responses. Our Random Forest Classifier model achieves an accuracy of 88.53%, with precision rates of 89% for those likely to seek treatment and 88% for those unlikely. Similarly, recall rates are 88% and 89%, respectively, with an F1-Score of 89% for both classes. This efficient performance underscores the model's capability to predict whether individuals will seek mental health treatment based on their responses.