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 bubble sort bubble sort linear search linear search for loop for loop binary search
The most appropriate algorithm to find the most frequent side effects in a set of raw medical data would be a for loop. This is because a for loop allows you to iterate through all the records in the dataset and count the occurrence of each side effect. By keeping track of the frequency of each side effect in a separate data structure (such as a dictionary or hash map), you can easily determine which side effects occur most often.
To find the side effects that occur most often in the given medical data, an appropriate algorithm would be a for loop. Here's why:
- Bubble sort and binary search are not suitable for this task. Bubble sort is used for sorting elements in a list, while binary search is used to find a specific element in a sorted list.
- Linear search can be used, but it may not be the most efficient algorithm as it would require iterating through the entire dataset for each side effect.
- A for loop can be used to iterate through the medical data and count the occurrences of each side effect. By keeping track of the frequency of each side effect in a separate data structure (such as a dictionary or a hash map), you can easily determine which side effects occur most often.
Therefore, the most appropriate algorithm would be a for loop.
To find the side effects that occur most often in the raw medical data, an appropriate algorithm would be a for loop combined with a dictionary or hash table.
Here's a step-by-step explanation of how you can implement this algorithm:
1. Initialize an empty dictionary or hash table.
2. Loop through each entry in the medical data.
3. For each entry, extract the side effects and check if it is already present in the dictionary.
4. If the side effect is already present, increment its count in the dictionary.
5. If the side effect is not present, add it to the dictionary with a count of 1.
6. Once the loop is complete, the dictionary will contain each unique side effect from the medical data as keys, and the corresponding count of occurrences as values.
7. Find the side effect(s) with the highest count(s) in the dictionary.
8. Return the side effect(s) with the highest count(s) as the most frequently occurring side effect(s).
This algorithm has a time complexity of O(n), where n is the number of entries in the medical data. It efficiently counts the occurrences of each side effect and returns the ones occurring most often.
Note: The other options (bubble sort, linear search, binary search) are not suitable for finding the most frequently occurring side effects in this case. Bubble sort is used for sorting, while linear search and binary search are used for finding specific values in a sorted or unsorted list, which is not the requirement here.