Discoveries from Exploring: Patient survival
Industry: Health and welfare services
Dataset: Heart Attack Mortality.csv
Explored on: 2024-03-08
Selected Goal Column: Mortality
There is a Strong Signal to predict Mortality - the best model on this data had +40.56 compared to guessing.
Most Likely Yes:
Anion gap>16.91 and
Rel failure>0.0
Most Likely No:
Anion gap<=16.91
Explored 50 factors over 1176 records, in which 13.52% had Mortality is Yes (159 of 1176).

Of these factors:
  • 2 were relevant for predicting Mortality.
  • 48 were found to be irrelevant or redundant, so were ignored.
  • Key Factors

    We analyzed each factor (column) in the data, looking for which factors matter and which are irrelevant in determining Mortality.

    Strongly Correlated Factors - In this dataset, we found some factors to be so strongly correlated with other factors that we could essentially swap them out and keep only one of them.

    Key Factors - The most important factors and their values more likely to result in 'Yes' for Mortality are:
    Factor Group Breakdown
    Anion gap
    Anion gap>=13.67

    Rel failure
    Rel failure<=0.0
    Irrelevant Factors - The following factors in your data were not very important in determining Mortality:
    3 INR

    Strongly Correlated With: PT
    4 Lymphocyte

    Strongly Correlated With: Neutrophils
    5 RBC

    Strongly Correlated With: hematocrit
    6 MCV

    Strongly Correlated With: MCH
    7 Urine output
    8 Platelets
    9 RDW
    10 Respiratory rate
    11 SP O2
    12 Systolic blood pressure
    13 Urea nitrogen
    14 PH
    15 atrialfibrillation
    16 deficiencyanemias
    17 depression
    18 diabetes
    19 gendera
    20 glucose
    21 group
    22 heart rate
    23 hypertensive
    24 age
    25 PCO2
    26 BMI
    27 Creatine kise
    28 Basophils
    29 Bicarbote
    30 Blood calcium
    31 Blood potassium
    32 Blood sodium
    33 CHD with no MI
    34 COPD
    35 Chloride
    36 Creatinine
    37 NT-proBNP
    38 Diastolic blood pressure
    39 EF
    40 Hyperlipemia
    41 ID
    42 Lactic acid
    43 Leucocyte
    44 MCHC
    45 Magnesium ion
    46 temperature
    Expand to learn more about Factors

    Certain factors (or columns) are more important when determining 'Yes' or 'No' for your Goal than others. You can optimize your time and resources by prioritizing key factors across data gathering, data management, and decision-making.

    For example, if data gathering is time-consuming or expensive, you might consider de-prioritizing irrelevant factors. Just be mindful that they may be important for other problems.

    If you need help, set up a call with our Berrijam AI Coaches to brainstorm how you can adopt these insights from factors.

    Key Segments

    A combination of factors can be used to define a segment, giving deeper insights and making them easier to act upon.

    We have automatically identified top segments for 'Yes' and 'No', by determining their importance based on the size of the segment and % Yes or % No within the segment. Alternatively, you can explore the segments below.

    More Likely Yes
    Segment Segment Breakdown
    Anion gap>16.91 and
    Rel failure>0.0

    Anion gap>16.91 and
    Rel failure<=0.0
    More Likely No
    Segment Segment Breakdown
    Anion gap<=16.91
    Explore More Segments
    Click on the rings to drill down into sub-segments and click the center circle to navigate back up.

    As you hover over a segment, you’ll see the factors that define the segment, the segment size as a percentage of all records, and the percentage of 'Yes' within that segment.
    Expand to learn more about Segments

    Segments help identify important groups or populations where to invest time and effort. For example, a teacher could use segments to focus on students who are least likely to pass the course. They also identify which combination of the factors and values to focus on if you want to increase or decrease the percentage of ‘Yes’.

    For example, a maintenance team might identify that machines operating above a certain temperature and load are more likely to break down. So they might set up temperature alerts and reduce the load at certain temperatures to keep the machine working.

    If you need help, set up a call with our Berrijam AI Coaches to brainstorm some ideas on how to adopt the insights from segments to design strategies or policies that are relevant to you.

    Model Results

    We explored 426 different models to find which produced the best results.

    Since there were no rows where Mortality was empty, we did not make any predictions with these models.

    The Best Explainable Model performed very well on this dataset:

    compared to guessing

    The Best Non-Explainable Model performed very well on this dataset:

    compared to guessing
    All Model Results

    Explored by using Heart Attack Mortality.csv on 2024-03-08 with Berrijam. Try it yourself at