Machine Learning and Binary Classification: f1 score is great in one class, terrible in another

I am using two methods (random tree and neural network) to calculate whether is person is suceptible to a certain disease, hence binary classification. After I run my code, I am getting a good f1-score for identitying the non-suceptible, for a terrible f1-score for identitying the suceptible:

          precision    recall  f1-score   support (Random Tree)

       0       0.86      0.99      0.92       711
       1       0.33      0.04      0.08       117

          precision    recall  f1-score   support (Neural Network)

       0       0.89      0.89      0.89       711
       1       0.31      0.30      0.30       117

I have tried a few things to increase the f-1 score, but to no avail:

1. Removing features that don’t strongly correlate with the label
2. Removing features that have significant outliers
3. Increasing size of neural network (added extra layer, f1-score went from 0.23 to 0.30)

What are other possible reasons why my f1-score for predicting suceptibility is terrible?

#Random Forest
rf = RandomForestRegressor(n_estimators = 199, random_state = 4)
rf.fit(X_train_scaled, y_train)
predictions = rf.predict(X_val_scaled)
y_val_hat_cat_rf = (rf.predict(X_val_scaled) > 0.5)
#Neural Network
model = keras.Sequential()
model.add(Dense(11, activation='relu'))
model.add(Dense(7,activation='relu'))
model.add(Dense(4,activation='relu'))
model.add(Dense(1, activation='sigmoid'))
hp_learning_rate = 0.01
model.compile(optimizer=keras.optimizers.Adam(learning_rate=hp_learning_rate),
            loss=keras.losses.BinaryCrossentropy(from_logits=True),
            metrics=['accuracy'])
model.fit(X_train_scaled,y_train,epochs=1000,verbose=0)
J_list = model.history.history['loss']
plt.plot(J_list)
val_acc_per_epoch = model.history.history['accuracy']