Suppose you are examining a set of raw medical data that includes the side effects of a specific drug. You are interested in finding out which side effects occur the most often. What would be an appropriate algorithm to find this data?(1 point)
Responses
linear search
linear search
for loop
for loop
binary search
binary search
bubble sort
The appropriate algorithm to find the side effects that occur most often in the set of raw medical data would be a frequency counting algorithm. This algorithm would iterate through the entire dataset and keep track of the frequency of each side effect encountered. By comparing and updating the frequency counts, the algorithm can determine which side effects occur most frequently.
None of the options provided (linear search, for loop, binary search, bubble sort) are specifically designed for finding the most frequently occurring side effects, so none of them would be the most appropriate algorithm for this task.
To find the most common side effects in a set of raw medical data, an appropriate algorithm would be a for loop.
A for loop allows you to iterate over each element in the dataset and keep track of the occurrence of each side effect. By comparing the occurrence of each side effect, you can identify the most commonly occurring ones.
Here's how you can use a for loop algorithm to find the most common side effects:
1. Initialize a counter or a dictionary to store the occurrence of each side effect.
2. Iterate through each record or entry in the raw medical data using a for loop.
3. For each record, check the side effects mentioned and update the counter accordingly. If a side effect is already in the counter, increment its count. If it's not already in the counter, add it with a count of 1.
4. After iterating through all the records, you will have a counter or dictionary that contains the occurrence of each side effect.
5. Sort the counter or dictionary in descending order based on the counts of each side effect.
6. Retrieve the side effects with the highest counts, which will be the most common side effects.
Using a for loop algorithm allows you to efficiently analyze the raw medical data and determine the most commonly occurring side effects.
To find out which side effects occur most often in a set of medical data, an appropriate algorithm would be a frequency count algorithm. Here's how you can implement it:
1. Create an empty dictionary to store the side effects as keys and their corresponding frequencies as values.
2. Loop through each entry in the medical data:
a. Check if the side effect is already in the dictionary. If it is, increment its frequency by 1. If it is not, add it to the dictionary with a frequency of 1.
3. After iterating through all the entries, you will have a dictionary containing the side effects and their frequencies.
4. To find the side effect(s) that occur most often, you can iterate through the dictionary and keep track of the maximum frequency encountered. Then, collect all the side effects with that maximum frequency.
This algorithm has a time complexity of O(n), where n is the number of entries in the medical data. It is a more appropriate choice than linear search or binary search because those algorithms are used to search for specific elements, not to count frequencies. Bubble sort is also not a suitable choice as it is a sorting algorithm, not a frequency counting algorithm.